# IFAC blog page

Dear IFAC Social media followers, Dear Friends and Colleagues,

Best wishes to you and your loved ones for the New Year. Our wish is that you will enjoy 2017 in good health, and that you will find it peaceful, prosperous and rewarding.

The social media platforms of IFAC are steadily gaining followers and reaching new audiences. It is great to count you among IFAC social media followers and I wish to express my sincere thanks for your continued support and your important contribution to the activities of IFAC

I warmly encourage you and encourage the IFAC Technical Committees to support the IFAC Social Media Strategy by following and retweeting/sharing the IFAC blog and participating in the discussions at the IFAC Twitter and Facebook accounts. Interested blog contributors are kindly invited to contact IFAC Social Media Liaison Jakob Stoustrup at jakob@es.aau.dk

I take great pleasure in inviting you to attend the 20th IFAC World Congress which will be held in Toulouse, France in July 2017. Everything is on track for what promises to be a highly rewarding, memorable and enjoyable event. I am happy to inform you that the IPC has received more than 4200 submissions and I look forward to meeting you all in Toulouse, France at the flagship event of our Federation.

With best wishes,

Janan Zaytoon

IFAC President

In this article, a closed-loop approach to human body weight control is presented. The main purpose of the article is to demonstrate that applying feedback has significant benefits over conventional open-loop techniques suggested in the rich health literature on the subject. In particular, a closed-loop approach is robust to the strongly adaptive mechanisms of the human body and to disturbances of various kinds. Also, in contrast to conventional approaches proposed in the health literature, the presented method based on feedback does not depend on any specific diet. In fact, the approach can be applied to any diet preferred by the subject.

DISCLAIMER: The proposed method has not been approved by medical doctors. If applied incorrectly, it can potentially cause significant health issues. It is strongly recommended not to pursue the experiments described below without consulting a physician.

Background

For a large and increasing proportion of the world population, overweight and obesity cause a wide range of health issues and in particular are leading causes of premature death. The World Health Organization (WHO) defines overweight as a Body Mass Index (BMI) greater than or equal to 25 kg/m2  and obesity as BMIs larger than 30 kg/m2. According to WHO, worldwide obesity has more than doubled since 1980. In 2014, almost two billion adults were overweight. More than 40 million children under the age of five were overweight or obese in 2014.

In part for health reasons, dieting has been recommended by medical doctors and other health experts for centuries, at least dating back to the early 18th century, e.g. by the English doctor George Cheyne, who based on personal experience recommended diets for anyone suffering from obesity or overweight as described in his 1724 report, An Essay of Health and Long Life. There is no shortage of descriptions of diets in contemporary literature, ranging from esteemed medical journals to popular magazines and newspapers. In Western societies, a significant proportion of the population has been following one or several such diets for longer or shorter periods of time. In addition to health challenges, overweight/obesity also have significant psycho-social effects.

In this article, we shall provide a control theory perspective on weight control. A simple feedback algorithm will be described below along with experimental data verifying the algorithm.

Modeling weight gain

The dynamics of human body weight is by far dominated by three factors:

• Food and drink intake, instantaneously causing a (partly temporary) weight gain
• Excretion, instantaneously causing a (partly temporary) weight loss
• Metabolism, slowly but steadily causing (temporary) weight loss

Recent research has shown that exhaust from lungs (part of excretion) is a major factor in weight loss. Burning 10 kg human fat requires inhalation of 29 kg oxygen. This produces 28 kg carbon dioxide and 11 kg water. As food and drinks are temporarily stored in the human stomach and bowels, the body weight is instantaneously increased with the weight of any food or drink consumed. Metabolism is usually divided into catabolism and anabolism, where catabolism is the process of breaking down organic matter and anabolism is the reverse process of constructing proteins and nucleic acids. Metabolism is catalyzed by enzymes and metabolic rates can be strongly time and state dependent, governed by such catalyzing enzymes. In eukaryotes, such as homo sapiens, metabolism is connected to a series of proteins in mitochondria. As a very coarse model, the level of metabolism at any given time is therefore proportional to the number of mitochondria. The number of mitochondria depends strongly on tissue types and therefore on body distribution of these, but in the larger picture the number of mitochondria is positively correlated with the number of cells in the body, which is finally approximately proportional to the body weight. In summary, this rough reasoning leads to the following extremely simple model for body weight dynamics:

$\frac{dw(t)}{dt} = -\alpha(w,t)\cdot w(t) – e(t) + f(t)$

where $$w(\cdot)$$>0 is the body weight, α is a positive parameter, depending on state and time, that governs the metabolism,  $$f(\cdot)$$ is the food/drink intake function, which can only attain non-negative values and $$e(\cdot)$$ is the excretion function, which can only attain non-negative values. Clearly, this model cannot be expected to be accurate in open loop. E.g. it does not capture the difference in dynamics between catabolism and anabolism, which would require a higher order model. Below, however, we shall argue that this very simple model surprisingly suffices to understand and design closed-loop behavior.

Proposed control algorithm

• Body weight is a measureable state variable
• Food weight is a controllable input
• Metabolic rates can be time-varying, but are bounded from below, α≥αmin

Based on these assumptions, a simple feedback control law that takes body weight as its measurement and specifies food intake as the control signal can be devised:

$F(t) = r(t+T) – w(t)$

where $F(t) = \int_t^{t+T}f(\tau)\;d\tau$is the weight of food and drinks consumed during a meal starting at time $$t$$ and ending at time $$t+T$$; further $r(t+T)$ is the control reference at time $$t+T$$ (the end of the meal). Since  $$f(\cdot)$$ is a non-negative function, the reference $$r(\cdot)$$ has to be chosen larger than $$w(\cdot)$$ at all times.  In practice, the algorithm can rely on (suitably conservative) estimates for some meals, if an insufficient number of measurements are available, as long as the integral constraint is met during a day – please, see experimental data below.

A consequence of the above is that a reference with a weight loss demand larger than that dictated by the metabolism (catabolism) at any period of time, is infeasible. In practice, however, the reference weight loss should not only be marginally smaller but significantly smaller than the metabolic weight loss between two consecutive meals, as otherwise the body will not get a sufficient amount of nutrients for sustaining normal operation, and potentially health will be challenged. Further, when choosing a reference, it should be taken into account that α tends to be monotonically increasing/decreasing with a monotonically increasing/decreasing w, i.e. metabolism tends to adapt in order to changing weight (this is well-documented in the medical literature).

Experimental verification

The algorithm described above was applied during an experiment with a duration 44 days with the author of this article as the subject. A reference was chosen that had a constant slope for the first 31 days (one month), followed by a constant value. The initial value of the reference was chosen as the initial condition of the body weight. The final value of the reference was chosen as a body weight that would bring the BMI from an initial 26.1 kg/m2 (mild overweight) down to 23.8 kg/m2, i.e. well into the normal (non-overweight) area. In summary, this schedule inferred a weight loss of 7.4 kg during the 31-day weight loss period, i.e. a daily decrement of 239 g, followed by a static weight condition for 13 days.

Figure 1: Results of closed-loop weight control experiment

The experimental results can be seen in Figure 1. In practice, the algorithm was carried out by three daily body weight measurements: a morning measurement, a measurement immediately before the last meal of the day, and a late evening measurement for validation. The breakfast and the lunch meals were chosen to weigh approximately half the margin between the (known) upcoming evening reference and the morning measurement. That left about half the food intake for the evening meal, which was weighed on the plate and calibrated to match the remaining margin up to the scheduled reference. With this approach, the reference was normally reached by each evening measurement. Figure 1 shows a few overshoots. These happened at events where adhering to social code prohibited the meals from being weighed, so estimates had to be applied instead. Also, drinks taken after the last meal were not calibrated, which gave minor deviations. The undershoots in the beginning of the experiment are deliberate.

It is interesting to note that as the actual weight approaches the target, the metabolic cycles decrease significantly in amplitude. This is probably due to body responses that change the metabolic rates. It is likely that such a mechanism has developed evolutionarily to respond to periods of food scarcity. In contrast, metabolism is seen to increase significantly close to the end of the experiment, where the flat part of the reference has allowed a much higher food intake, causing the body to respond by what could perhaps resemble a food surplus scenario in an evolutionary context. Throughout the experiment, the conscious awareness of the subject provided another level of feedback, as the subject gained experience with the impact of his exercise, food composition, etc.

Conclusions

The closed-loop weight control approach proposed in this article has the virtue of offering deterministic results, based on the single assumption that the algorithm is followed strictly. It should be noted that the method is completely independent of any specific composition of the diet. In fact, although the daily weight loss of the experiment was significant, the involved diet throughout the experiment included a proportion of energy intensive food components such as chocolate and red wine (the red wine is discernible in the experimental results, causing the metabolic cycles to reduce significantly in two instances). Also, it should be noted that due to monotonicity properties of metabolic systems, any diet that has the same weight loss as in the described experiment, will have the exact same average food intake, provided the food has the same distribution of proteins/carbs/fats.

A limitation of the proposed approach is that it does not address nutritional adequacy aspects of diets. If one tries to lose weight too fast, or put a goal for a too-low ultimate steady state reference, one’s health will suffer. In practice the reference trajectory should be “reasonable.”

On the other hand, as an important conclusion of this article, feedback can be combined with any given diet, providing a layer of mathematically guaranteed weight loss to a physiology based diet that would typically be composed from a health perspective. The main approach to a healthy body will always be a healthy lifestyle with healthy food and lots of exercise. However, for anyone on a diet, there is simply no reason not to embed the diet in a closed-loop approach and take advantage of the power of feedback!

Article provided by:
Jakob Stoustrup
Department of Electronic Systems
Automation & Control
Aalborg University, Denmark
IFAC Technical Board


Many phenomena are common to us all, but the way they work might be less well known. Why? They are dynamic systems! What is a dynamical system? Generally, it means that such kind of systems are described by Partial Differential Equations (PDE), and in order to study them, we have to understand their properties and we need to control some of them. We need to simulate the control developed in order to be sure that it fits exactly what it is expected, or to understand how a phenomenon is going on!

This intriguing area will be studied in a recently granted project “DYCON–Dynamic control”, which aims to develop a multifold research agenda in the broad area of Control of Partial Differential Equations (PDE) and their numerical approximation methods by addressing some key issues that are still poorly understood. To this end we aim to contribute with new key theoretical methods and results, and to develop the corresponding numerical tools and computational software.

The field of PDEs, together with numerical approximation and simulation methods and control theory, have evolved significantly in the last decades in a cross-fertilization process, to address the challenging demands of industrial and cross-disciplinary applications such as, for instance:

• The management of natural resources (water e.g.),
• Meteorology (make better weather predictions e.g., which involves big data problems and related numerical problems),
aeronautics,
• The oil industry (oil forage e.g., whose main problem is the friction at the bit),
Biomedicine (cancer strategy via immunotherapy),
• Human and animal collective behaviour, (understand behaviour of bees in order to anticipate their extinction, relations and interactions between several species) etc…

The ERC Advanced Grant DYCON project identifies and focuses on six key topics that play a central role in most of the processes arising in control applications, but which are still poorly understood: control of parameter dependent problems; long finite time horizon control; control under constraints; inverse design of time-irreversible models; memory models and hybrid PDE/ODE models, and the links between finite and infinite-dimensional dynamical systems.

These topics cannot be handled by superposing the state of the art in the various disciplines, due to the unexpected interactive phenomena that may emerge, for instance, in the fine numerical approximation of control problems. The coordinated and focused effort that we aim at developing is timely and much needed in order to solve these issues and bridge the gap from modelling to control, computer simulations and applications.

The ERC Advanced Grant DYCON provides resources to researchers willing to contribute to these endeavours within the research team led by Enrique Zuazua at Universidad Autónoma de Madrid-Spain.

Researchers interested in cooperation are welcome to get in contact with Enrique Zuazua (enrique.zuazua@uam.es, www.enzuazua.net).

There will be openings and opportunities for researchers in all career stages: internship PhD students from other centers and groups, PhD and postdoctoral contracts and one-quarter visiting positions of confirmed researchers.

Article provided by:
Valérie Dos Santos Martins
Laboratoire d’Automatique et de Génie des Procédés,
Université Claude Bernard Lyone
TC 2.6. Distributed Parameter Systems


At its 2014 World Congress, IFAC launched a “Pilot” Industry Committee with the objective of increasing industry participation in and impact from IFAC activities. I chair this committee with the support of Roger Goodall (Loughborough University, UK) and Serge Boverie (Continental, France) as co-chairs. This committee was established as an outcome of an Industry Task Force led by Roger Goodall in the last triennium.

In 2015 the committee undertook a survey of its members to get their views on the impact of advanced control and challenges associated with enhancing the impact. The survey had two questions. 23 of our 27 members then (excluding the chair) responded. The majority of the membership is either currently with or has prior affiliation with industry; all others have had substantial industry involvement as well. Most of the members were nominated by IFAC National Member Organizations and Technical Committees.

Although limited in many ways, I thought the survey responses would be of interest to the controls community.

##### Survey Question 1: Impact of Specific Advanced Control Technologies

First, we asked for members’ perceptions about the industry success (or lack thereof) of a dozen advanced control technologies. PID control was also included in the list for calibration purposes. A glossary was included with the survey, listing topics covered under each technology. Members were asked to assess the impact of each of these technologies by selecting one of the following:

• High multi-industry impact: Substantial benefits in each of several industry sectors; adoption by many companies in different sectors; standard practice in industry
• High single-industry impact: Substantial benefits in one industry sector; adoption by many companies in the sector; standard practice in the industry
• Medium impact: Significant benefits in one or more industry sectors; adoption by one or two companies; not standard practice
• Low impact: A few successful applications in one or more companies/industries
• No impact: Not aware of any successful deployed real-world application

The results: The control technologies are listed below, in order of industry impact as perceived by the committee members:

Rank and Technology High-impact ratings Low- or no-impact ratings
1. PID control 100% 0%
2. Model-predictive control 78% 9%
3. System identification 61% 9%
4. Process data analytics  61%  17%
5. Soft sensing 52% 22%
6. Fault detection and identification 50% 18%
7. Decentralized and/or coordinated control 48% 30%
8. Intelligent control 35% 30%
9. Discrete-event systems 23% 32%
10. Nonlinear control 22% 35%
12. Robust control 13% 43%
13. Hybrid dynamical systems 13% 43%

On the face of it, these results are disappointing. No advanced control technology is unanimously acknowledged by industry-aware control experts as having had high industry impact—90 years after its invention (or discovery), we still have nothing that compares with PID! It’s also concerning that the “crown jewels” of control theory appear at the bottom of the list.

However, the fact that all the technologies had at least some positive assessments suggests that the impact could well be higher than indicated: Many control scientists and engineers are likely not aware of the impact of control technologies outside the application domains of their experience. Thus the problem may be as much the perception as the reality.

##### Survey Question 2: Issues and Challenges with Industry Impact

The second question listed a number of statements and asked respondents to indicate their level of agreement with each. Agreement could be indicated as strongly agree, agree, neutral, disagree, or strongly disagree.

The statements and the levels of agreement are tabulated below. I have also noted any significant differences of opinion between the industry and academic members of the committee.

Industry lacks staff with the technical competency in advanced control that is required for high-impact applications 83% 4%
Control researchers are much poorer than researchers in other fields at communicating their ideas and results to industry management 26% 30%
The maturity or readiness level of results of advanced control research is too low for attracting industry interest 57% 22% 42% of industry respondents but no academic respondent disagreed
Advanced control has limited relevance to problems facing industries and their customers 4% 65%
The conflict between industry deadlines and academic research timelines is worse in control than in related engineering fields 30% 35%
Control researchers place too much emphasis on applied mathematics or advanced algorithms whereas successful industry applications require deep domain knowledge 83% 13%
Control researchers place too little emphasis on plant/process modeling and model-development methodologies 57% 17% No one from industry disagrees 30% of academics disagree
Students in control (undergraduate and graduate) are not sufficiently exposed to problems in industry 70% 13% No one from industry disagrees 30% of academics disagree
The academic control community is not seriously interested in collaboration with industry 26% 39% 33% of industry respondents but only 11% of academic respondents agree
There is no problem—advanced control is successful and appreciated in relevant industries 13% 83%

A clear message is that domain understanding/modeling is crucially important but not adequately pursued and taught. Neither expertise nor experience in advanced control per se is sufficient to realize industry impact.

##### Conclusions

This survey wasn’t, and nor was it intended to be, scientific or comprehensive, but I and my fellow committee members have found the results thought- and discussion-provoking. We are continuing to explore the challenging problem of industry impact from control research. Among other outputs, we expect to recommend specific enhancements to IFAC events, publications, and volunteer groups. Your feedback is welcome and will be appreciated!

Article provided by:
Senior Fellow
Honeywell/W.R. Sweatt Chair in Technology Management
The University of Minnesota
Vice chair, IFAC Technical Commitee


Consumers are expected to play a considerable greater role in smart grid deployment and it is crucial to boost their awareness of this more active role. Smart grid is a great opportunity for all consumers, whose involvement in demand side management will significantly speed up the development of a smart grid market. The way the energy is used has to be revolutionised and, to actualize that, consumers need to understand what benefits they will achieve and how to change their behaviour to gain those benefits. All the players in the electricity system need to learn how to engage and effectively educate consumers, and improve their trust. We do not know the best way to make this happen yet, but we do know the highly negative impact of inadequate consumer engagement on future deployment plans. Thus, control solutions and automation systems for demand side management necessitate taking consumers into account, their preferences, their needs and uncertainty in their behaviour.

The next-generation electric grid needs to be smart and sustainable to deal with the explosive growth of global energy demand and achieve environmental goals. To effectively smarten the grid we need to rethink the roles and responsibilities of all players in the electricity system. This smartening is a progressive and revolutionary process (Figure 1). However different settings will be around the world and deployed at different rates, the use of information and communications technology to monitor and actively control generation and demand in near real-time is indisputably a common feature. Therefore, control and automation are essential for enabling consumers to actively support the grid.

Figure 1. Smarter electricity systems (source: IEA, 2011) [Click on image to view larger version]

The increased control over the network can enable a wider, more sophisticated range of smart methods and innovative schemes, such as demand response and smart energy management systems for buildings, to facilitate local management of demand and generation. Demand response includes both manual and automated consumer response, smart appliances and thermostats, which are able to respond to price signals, or carbon-based signals. These smart devices are connected to an energy management system or controlled directly by the utility or a system operator. Smart energy management systems for buildings need to incorporate the user into the design and thus be responsive to their occupants in order to improve their comfort and allow smart appliances and heating systems to be on the market and respond to price signals to help decreasing the electricity bills. The benefits for consumers can be diverse, e.g., reduction of the electricity bill, improving of living conditions, supporting a more environmentally friendly energy behaviour.

In particular, smart energy management systems are required to be able to:

• respond to signals from the grid and take action on this basis (e.g., decreasing energy use when prices are high or automatically shifting consumption to times when prices are lower);
• manage local generation facilities, such as solar panels, and fed back into the grid any energy
• optimally schedule storage devices, which can be used to balance out the smart grid.

Those advanced and innovative energy management systems make buildings smart and we can claim that a smart grid cannot exist without smart buildings. Hence, there will be more and more active roles for consumers of different sizes to play in a smart grid, for instance:

• Residential consumers can choose among different tariff schemes and optimally shift smart appliance demand away from peak times through smart meters and energy management systems;
• Industrial and commercial consumers can participate in the energy market through
a wide range of demand response schemes;
• Generator owners can participate in demand response schemes and the market by supplying needed energy to the grid.

Novel control and automation systems are becoming quite widespread, although standardised solutions are still not available, which means that expensive tailored configuration are required. This clearly limits the engagement of consumers, in particular small-scale consumers. In addition to designing and deploying control and communication solutions more affordable to a wider range of consumers’ sizes, effective motivational factors must be explored and thoroughly examined (e.g., environmental concerns, better comfort, control over electricity bills). The risk here is that consumers who do not make the savings expected from their behavioural change might consider the whole experience disappointing and frustrating.

Accurate, systematic and methodical research and evaluation are still needed to identify the optimal methodology to understand better the interaction between consumers and energy market, as well as the effect of enabling technologies for smart grid deployment.

A persistent behavioural change is vital to effectively enable smart energy technology development. We still need an answer to the following questions:

• Is there an optimal mix of behavioural change, consumer feedback and automation technologies?
• How much customer education is required and what are the best approaches?
• Which types of automated demand response schemes are most useful to different types of customers (residential, commercial, industrial)?

Research groups, along with industry and governments, need to design and test more consumer-focused control solutions that can foster large-scale consumer behaviour change.

Article provided by:
Alessandra Parisio
School of Electrical and Electronic Engineering
The University of Manchestervice
IFAC Technical Committee 9.3 (Control for Smart Cities)


Over the last three decades, the pervasiveness of engineering communication systems, enabled by the development of cheap sensing and computation capabilities, lightweight battery storage and low power actuation systems, has exploded. Concurrently, advances in health care and medicines have and will continue to lead to populations with ageing demographics throughout the industrialised world.

One impact is that the current generation of retirees will live longer and is arguably the first to have grown up with communication and automation technologies as an integral part of everyday life. As a consequence, there are significant opportunities to develop assistive technologies based around automation systems that provide both better quality of life and lower medical costs as user acceptance of the developed technologies will likely be high. Of critical importance is the engagement with the likely users during the development process to ensure that interfaces and social aspects are properly identified. Of particular relevance is the need to avoid overly intrusive approaches, by capitalizing on the embedded nature of suitable technology, and to undertake co-design with the target groups.

The opportunities for automation and ICT technologies then range from non-intrusive detection of incidents, subsequent intervention through partial or potentially complete mitigation of damage through appropriate actions, to assistance in recovery from incidents through appropriate rehabilitation.

As an illustration of the potential benefits of seamless integration of automation, (and one of many that could be highlighted) we can consider falls in the elderly. These represent by far the most common preventable incident with serious consequence for over 65s, and account for a clear majority of hospitalisations in this age group.

From a detection standpoint, there is an opportunity to leverage smartphone uptake (recent surveys have indicated the vast majority of the elderly today own smart phones). The integration of multiple sensors into phones has been already exploited by a number of apps for background fall detection. In essence, these use relatively simple algorithms with thresholds set on acceleration measurements from the inertial measurement units integrated in the phones to trigger the detection of an event. With the introduction of smart watches carrying their own sensors false classification rates will be lowered, as it is easier to prevent false classifications if sensor position is known to be constant with respect to the user’s body.

Naturally, the detection of a potentially injurious event should be coupled with the ability to mitigate the damage. Already in the existing applications, detection of a potential fall via the phone is used to trigger an alert sent to a designated person(s) along with GPS data describing the wearer’s location, typically with a short delay after a fall is detected, where the owner has the opportunity to correct for a false positive classification. Smart phones can also automatically undertake a tiered call response cycle under these conditions, from family, to emergency services.

One of the most serious consequences of falls amongst the elderly is fracture of the femoral head – a more common occurrence as bone density and reaction time of the person (which would aid in in situ fall mitigation) are typically both diminished with age. Furthermore, the complications associated with the surgical procedure for femoral repair lead to significantly increased mortality risk in the elderly. Mitigating the effect of a fall through cushioning the femur in at-risk areas is possible through passive protection, however active devices operating on an air-bag principle activated on fall detection through smart sensing and classification may provide a less intrusive system with better damage mitigation.

Of even greater potential than mitigating a fall is the design and development of devices that can prevent it. Such a system requires early detection before action is taken, thereby relying on more sophisticated sensor feedback and smarter integration with the wearer’s natural actuation capability, so as to provide assistance when required. Already there are commercial prototype systems such as the Hybrid Assistive Limb (HAL) developed by Cyberdyne (no not (yet) the Terminator company!) aiming to do so, which rely on conventional robotic architectures to provide functionality.

Usability will only increase as the man-machine interface is continually refined. Assistive technologies include mechatronic aids, which can assist rather than entirely prevent falls: a lightweight mechanical assistive support device could achieve this, as an intelligent reconceptualisation of the once familiar polio leg braces. However the advent of wearable sensors and soft robotics offers perhaps greater potential due to reduced weight and subsequent reduced on-board power requirements, as demonstrated in prototype systems such as those under development at various research institutes.

Finally, bio-mechatronics offers great potential in targeting rehabilitation strategies towards individual patients. The opportunities include using available sensing technologies to record real world activity, which can be interpreted by clinicians to gauge and improve recovery. Assistive therapeutic aids which initially enhance patient capability through EMG feedback and transition across to systems that retrain or strengthen muscles, thereby reducing the probability of further injurious events. Such approaches may partially alleviate the need for physiotherapy to be conducted wholly onsite, thereby reducing treatment costs and also improving recovery rates.

So while medical and health sciences can claim some responsibility for creating the (nice) problem of an increasing ageing segment of the population, it is perhaps engineering and automation technologies that are going to play a major role in assisting that population to continue to live active and fulfilling lives. It is, however, critical that the age groups concerned play an active participatory and responsible role in the codesign of devices and automation being created for their benefit.

Article provided by:
Prof Chris Manzie
Department of Mechanical Engineering
University of Melbourne
IFAC TC 4.2 (Mechatronic Systems) and 7.1 (Automotive Control)

Traffic Control Centres (TCC) are expensive pieces of infrastructure tasked with the problem of sensing, surveying, monitoring, and actively interfering with traffic flow in road networks.

Figure 1 provides a broad overview of how a TCC operates. The controlled system is
a network of roads equipped with sensors and control effectors. Two-way flow of information from and to the field is effectuated by the IT infrastructure maintained by the TCC. Network operators are managing traffic in real time based on streams of information converging to the traffic control room. They have to decide which objectives and policies to support and how to implement them by managing the available control device.

Figure 1: schematic of a TCC.

This is a most challenging and highly complicated task, encompassing diverse hardware and software systems, which have to be operated following specific regulations and procedures, in support of policies and objectives defined by network operators or wider political bodies. The complexity of the traffic flow management problem is due to the often chaotic nature of human behaviour, the diverse needs generating the individual trips, the constraints imposed by regulations, e.g. safety, and the objectives TCC pursue, e.g. delay minimization or emissions reduction.

Different control architectures can be conceptualised for performing the same tasks. Currently, the most common architecture adopted by TCC owners is that of a centralised control structure, allowing room for decentralised operations under strong supervision. A lot of money have been invested in this kind of infrastructure resulting mostly in static networks of sensors (loop detectors, CCTV etc.) and control effectors (traffic lights, variable message signs etc.). Usually, it is within this framework that control systems for particular traffic management applications are designed.

With the advent of highly equipped vehicles and vehicle automation, Vehicle Automation and Communication Systems (VACS) are changing the system architecture of traffic management. VACS are treasure coves of information as a lot of data can be extracted that can help address a variety of needs, e.g. commercial, infotainment and traffic management. From the control engineering approach such information is of little help unless it is explicitly used for positively interfering with traffic in real time.

In this sense, VACS are becoming both the sensor and the control device. They are both the means of information collection and transmission, and of actively interfering with traffic. Operating within a highly robust, secure and high-performance communication network, static sensors and control systems will become obsolete and a memory from the past or at a best a fall-back system. Fundamentally different operational requirements are in effect compared to those of centralised architectures posing new challenges for control design of network-wide vehicular flow.

The control technology for completely automating a vehicle is largely available.
Of course there are challenges, see e.g. a previous entry in IFAC’s blog (Link here). However, going from the individual vehicle to the aggregate behaviour of several thousands of vehicles and control of their collective interaction, is an entirely different control problem and in many respects more difficult to address. A fundamental change in thinking tailored for this new road / communications infrastructure / vehicle / driver system is necessary.

Many different scenarios can be envisaged, including:

• The compulsory intervention by a TCC authority to vehicle controls. This implies that full control of the vehicle is delegated to a traffic authority. Acceleration, speed and position trajectories are decided by a higher level system supervising an area and deciding on the optimal, according to some societal notion of cost, vehicle operation. Dedicated lanes for segregating manual and autonomous vehicles could be used as well, although this is very difficult particularly for urban environments.
• Partial intervention by a TCC authority. In this case vehicle control is assumed (or partially assumed) by a traffic authority should certain conditions arise, e.g. a congested road section or around an area near the approaches of an intersection.
• Freely acting informed drivers. In this case, it is the drivers’ intelligence that takes over as a regulator of traffic under the influence of information communicated to them through an appropriate human machine interface. This scenario does not exclude the use of autonomous vehicles, but the decision of allowing a traffic authority access and control of a vehicle is left at the driver’s discretion.

#### Are you ready to give up control of your car for the sake of traffic management? Are you willing to delegate your vehicle’s control to a different authority, other than you?

Although the answer seems to be “yes” when this question arises in the context of the individual vehicle platforms, it may not be so when it is posed in the context of everyday commuting and travelling. Leaving aside institutional and legal issues, there is this question of whether people will accept losing their freedom of action operating their own car. There are situations where a “yes” or a “no” seem to be clear. When you are stuck in a solid block of congestion and you are immersed in a stop-and-go situation, it seems much preferable to either use the car as an office and work on the computer or as a TV set and watch a movie, leaving the vehicle to crawl its way to the destination. When riding in the countryside, a lot of people would respond with a “no” as they would drive manually themselves just for enjoying the experience.

But what happens when while commuting to work you believe that what is suggested or the way your vehicle is operated (lets say by a TCC) is not the best for you? It may be the best on a societal benefit level (although not necessarily so), i.e. the “common good”, but not on an individual level. Many people will answer “no” to this question, irrespective of whether we think of this as an egoistic response. Furthermore, the very notion of been forced to allow access and delegate control of an object considered private may be unacceptable by a lot of people from the general population. They cannot be neglected nor their choice be banned since they are legitimate road users. Their existence shapes the properties of the traffic flow process and hence they affect control design. In other words, there are strong cultural issues involved, which affect the efficiency of any large area traffic control design.

Designing vehicle based control systems supporting autonomous operations requires focusing primary on the individual vehicle; but designing network-wide traffic management controllers requires focusing on the broader picture of spatio-temporal traffic dynamics and on the way individual vehicles interact with other vehicles and the infrastructure. All three scenarios outlined pose daunting challenges from the technical side, even if autonomous vehicles allow us to treat them as “ballerinas” in the daily commuting dance. The scenario of freely acting informed drivers, although the most challenging of the three, seems the most appropriate, politically rewarding and easier to promote to the public.

Article provided by:
Apostolos Kotsialos apostolos.kotsialos@durham.ac.uk
School of Engineering and Computing Sciences
Durham University, United Kingdom
IFAC TC 7.4 (Transportation Systems)

Education is changing rapidly, both due to an increased understanding of pedagogy and also the potential offered by new technology to do things better.

For example, there is an increasing understanding that student activity and involvement is critical for effective learning and thus a move away from the dominant reliance on traditional lectures to increased use of more interactive engagement activities . Another part of pedagogy that is receiving great current interest is the topic of feedback, for example, what feedback do students need to support effective learning and how can this feedback be provided efficiently and rapidly?

Within engineering a classic engagement activity was paper and pen based problem solving, however this has the disadvantage of providing relative slow (wait for a tutor meeting) or fast but low quality feedback (such as right/wrong). Advances in technology and in particular universal access to powerful computing devices (phones, laptops, …) provide opportunities for staff to develop interactive learning environments which give immediate and high quality feedback thus allowing students to become more effective independent learners. It is gratifying to know that control researchers [4] are leading the global field in these developments.

The following gives some examples of how control engineers are embedding effective teaching pedagogies which encourage and facilitate student activity, reflection and independent learning. The main focus is on student activity by way of ‘free’ access to laboratory equipment so that students can easily apply their learning and experiment unhindered by rigid timetable constraints and closed laboratory instruction sheets.

#### Exploiting the Internet of Things for Control Education: virtual and remote laboratories.

The Internet of Things (IoT) is the network of physical devices which are connected to the Internet. These devices can therefore be accessed remotely: whether it is just for monitoring purposes of such objects and/or their surroundings or, moreover, even for controlling some of their aspects.

The impact, applications and importance of the IoT have been growing over the last few years, as the technology has been progressing. In Google, a search for “internet of things” gives no less than twenty-two million results. Among them, articles in pages from companies and journals as important as Forbes, Microsoft, The Guardian, Wired, Intel or Cisco can be found. In Google News, just in the last week (at the moment of writing these lines), there is news on the IoT in Fortune, Bloomberg, TheStreet or TechRadar, for example. The huge number of references to the IoT as well as the wide variety of places in which this topic is covered (from technological journals and webpages to society and financial ones), give a good idea of the general interest that exists for this concept. Also, according to a 2015 research from Tata Consultancy Services, 26 companies plan to spend $1 billion or more each on IoT initiatives this year, while, according to the McKinsey Global Institute, the IoT has a total potential economic impact of$4 trillion to \$11 trillion a year by 2025, which represents around 10% of the world economy.

Virtual and Remote Laboratories (VRLs) are part of the IoT. A RL uses laboratory equipment which is connected to the Internet so that teachers and students can operate it at distance. RLs are the most immediate application of the IoT to education, especially in those fields where experimentation is a key part of the learning process, such as it is in Control.

If you are interested in the topic we invite you to visit the UniLabs portal on virtual and remote labs (http://unilabs.dia.uned.es/). As an example below you can find the interface of the Control of the Ball and Hoop system.

#### Control of the ball and hoop system

The Ball and Hoop system is an electromechanical device consisting of a ball rolling on the rim of a hoop. The hoop is mounted on the shaft of a servomotor and can rotate about its axis. The rotation of the hoop causes an oscillatory movement of the ball around its equilibrium point. The behaviour of the ball is similar to the dynamic of a liquid inside a cylindrical container.

The main objective of this system is to control these oscillations. With this laboratory you can perform, among others, these tasks and activities:

• Study the transmission zeroes and non-minimum phase behaviours
• Velocity and position control of the hoop
• Control of deviation of the ball from its rest position

#### Take home laboratories

One obvious mechanism for improving student access to equipment is by allowing students to take equipment home, thus enabling them to perform open-ended tests at will. In recent years, data acquisition and control hardware has become relatively cheap and this is a key enabler for development of affordable laboratory equipment which can be produced in multiples of 10 or even a 100, thus allowing every student to have one!

###### Remote laboratory

Staff in the University of Sheffield have developed a platform [6] for supporting the learning and application of skills crossing topics such as state-space design, state estimation, modelling, classical control and labview which also is embedded around an application area of obvious interest of Aerospace engineers (https://www.youtube.com/watch?v=mudKnc6v07E). The hardware consists of a miniature three-degree-of-freedom (3DOF) helicopter, interfaced to a PC via a NI myDAQ. The construction allows for easy assembly, and disassembly so students can take home, or indeed use on any University computer. The entire parts cost of each kit was under £300, making it possible to provide each student with his or her own kit, on a loaned basis. The equipment has now been used by 4 different cohorts of students and the general feedback is overwhelmingly positive, with students greatly appreciating the opportunity to put advanced theory into practice upon a challenging real-world system, in a time and place of their choosing.

#### Internet Based Control Education Conference

The IFAC Workshop on Internet Based Control Education (http://ibce15.unibs.it/) was held in Brescia, Italy, from 4th to 6th November 2015, organized by the University of Brescia (Italy) in cooperation with Multisector Service and Technological Centre (CSMT), Brescia (Italy). IBCE15 has been sponsored by the IFAC Technical Committee on Control Education (TC9-4) and co-sponsored by the IFAC Technical Committee on Computers for Control (TC3-1) and by the IFAC Technical Committee on Control via Communication Networks (TC3-3). The workshop has served as an international forum for interaction among engineers, scientists, and practitioners of control engineering who are interested in adopting and promoting internet-based methodologies for teaching control engineering. About 50 papers have been presented and the main topics addressed were virtual and remote labs, interactive tools, problem-based learning and internet-based control education assessment, and web-based educational environments. In general, there was a clear recognition that internet-based teaching methodologies can significantly enhance the learning of the students but they should be put in the right context in order to be fully appreciated. It is also clear that sharing the resources can greatly simplify the work of the teacher.

Some of the attendees of the IFAC Workshop on Internet Based Control Education at the Mille Miglia museum in Brescia.

Article provided by
Sebastián Dormido sdormido@dia.uned.es Chair of the Technical Committee TC 9.4
J. Anthony Rossiter j.a.rossiter@sheffield.ac.uk vice-chair of Technical Committee TC 9.4
Bryn Ll Jones b.l.jones@sheffield.ac.uk
Antonio Visioli antonio.visioli@unibs.it Chair of IBCE 2015


The idea of autonomous cars has been in the air as early as the 1920s, but the first prototypes of truly autonomous (albeit limited in performance) road vehicles appeared only in the 1980s. Since then, several companies, e.g.,Mercedes, Nissan and Tesla, as well as many universities and research centres all over the world, have pursued the dream of self-driving cars. More recently, a few ad-hoc competitions and the increasing interest of some big tech companies have rapidly accelerated the research in this area and helped the development of advanced sensors and algorithms.
As an example, consider that Google maintains (and publishes) monthly reports including the experimental tests and the most recent advances on its driverless car.

The reasons why such a technology is not yet on the market are many and varied. From a scientific point of view, autonomous road vehicles pose two major challenges:

• a communication challenge: how to interact with the surrounding environment, by taking all safety, technological and legal constraints into account?
• a vehicle dynamics challenge: the car must be able to follow a prescribed trajectory in any road condition. On the one hand, the interaction with the environment mainly concerns sensing, self-adaptation to time-varying conditions and information exchange with other vehicles to optimize some utility functions (the so-called “internet of vehicles” – IoV).

These issues undoubtedly represent novel problems for the scientific community and have been extensively treated in the past few years. On the other hand, control of vehicle dynamics may seem a less innovative challenge, since electronic devices like ESP or ABS are already ubiquitous in cars.

Within this framework, robust control, namely the science of designing feedback controllers by taking also a measure of the uncertainty into account, has played a central role. However, by taking a deeper look at the problem, it becomes evident that the main vehicle dynamics issues for autonomous cars are more complex than those concerning human-driven cars and the standard approaches may be no longer effective.

Actually, path planning and tracking is a widely studied topic in robotics, aerospace and other mechatronics applications, but it is certainly novel for road vehicles. In fact, in existing cars, even the most cutting-edge technology devices are dedicated to adjust vehicle speed or acceleration in order to increase safety and performance, whereas the trajectory tracking task is always fully left to the driver (except for few examples, like automatic parking systems).

Nonetheless, most of vehicle dynamics problems arise from the fact that the highly nonlinear road- tire characteristics is unknown and unmeasurable with the existing (cost-affordable) sensors. Therefore, keeping the driver inside an outer (path tracking) control loop represents a great advantage in that she/he can manually handle the vehicle in critical conditions (at least to a certain extent) and make the overall system robust to road and tire changes. This is obviously not the case for autonomous vehicles.

Hence, it seems that standard robust control for braking, traction or steering dynamics could turn out to be “not robust enough” for path tracking in autonomous vehicles, because one can no longer rely upon the intrinsic level of robustness provided by the driver feedback loop. In city and highway driving, this fact may not represent a problem, because the sideslip angles characterizing the majority of manoeuvres are low and easily controllable [8]. However, in the remaining cases (e.g., during sudden manoeuvres for obstacle avoidance), a good robust controller for path tracking, exploiting the most recent developments in the field, could really be decisive to save human lives in road accidents.

It can be concluded that still a few important questions need an answer by robust control people, e.g.:

• “can we provide a sufficient level of robustness with respect to all roads and tire conditions, without decreasing too much the performance?”
• “are we able to replicate the response of an expert driver to a sudden change of external conditions?”
• “how can we exploit at best the information coming from the additional sensors usually not available on-board (e.g., cameras, sonars…)?”

but also many others.

IEEE experts estimate that up to 75% of all vehicles will be autonomous by 2040. This scenario will be accompanied by significant cost savings associated with human lives, time and energy. As control scientists and engineers, it really seems we can play a leading role towards this important social and economic leap.

Article provided by
Simone Formentin, PhD, Assistant Professor
IFAC Technical Committee 2.5: Robust Control


The Robot based Autonomous Refuse handling (ROAR) project is a first attempt to demonstrate such an autonomous combination. An operator driven refuse collection truck is equipped with autonomous support devices to fetch, empty, and put back refuse bins in a predefined area.

The physical demonstrator in the ROAR project constitutes one truck and four support devices. When the truck has stopped in an area, a camera-equipped quadcopter is launched from the truck roof to search for bins and store their positions in the system. As bin positions become available in the system, an autonomously moving robot is sent out from the truck to fetch the first bin. The system’s path planner calculates the path to the bin as an array of waypoints. The planner calculates paths based on a pre-existing map of the area. Upon following the waypoints, the robot is intelligent enough to avoid obstacles that are not on the map. To accomplish this detection, the robot is equipped with a LiDAR and ultrasonic sensors.

After reaching the last waypoint, the robot changes from navigation to pick-up mode. By exploiting the LiDAR and a front facing camera, the exact position and orientation of the bin can be detected. The robot aligns itself so that the bin can be picked up.
After the pick-up, the planner provides the robot with a new path back to the truck. After the last waypoint, the robot aligns with the lift at the rear of the truck. The lift is set at a pre-defined angle, so that the robot can move up to the lift and hook the bin onto it. During the emptying of the bin, the lift system monitors the area around the lift with a camera to assure that no person is in the way for the lift. If so, the lift movement is paused until the area is clear.

An emptied bin is picked up by the robot and returned to its initial position, once again based on a path from the planner. When reaching the initial bin position, the bin is put down. The robot can thereafter move to the next bin to be emptied, and the emptying procedure is repeated.

When there are no more bins to empty, the robot moves back to the truck and aligns itself with the lift. Similar to a bin, the robot is hooked on to the lift and the overall procedure is completed. The truck can thus be started and be driven to the next area.
The coordination of the truck and the support devices is based on a discrete event system model. This model abstracts the overall emptying procedure into a finite number of states and transitions. The states capture distinguishable aspects of the system, such as for example the positions of the devices and empty/full states of the bins. The transitions model start and completion of the various operations that the devices can perform. All steps in the above description of the emptying procedure can be modeled by such operations.

The investment in the discrete event model carries a number of attractive properties. During the development phase, the model can be derived using formal methods. Verification as well as synthesis (iterative verification) is then employed to refine an initial model to satisfy specifications on the system.

Moreover, the development of the actual execution of an operation can be separated from the coordination of the operation. As an example, consider the operation modeling that the robot navigates along a path. From an execution point of view, the operation must assure that given a path the robot eventually ends up at the last waypoint without colliding with any obstacle. From a coordination point of view, the operation must only be enabled when there is a path present in the system and the robot is positioned close to the initial waypoint.

The model contains two types of operations; operations that model the nominal behavior, and operations that model foreseen non-nominal behavior. The recovery operations in the second group can for example describe what the system can do when the robot cannot find a bin at the end of a path, or how to re-hook an incorrectly placed bin on the truck lift.

The discrete event model can also be exploited to handle more severe recovery situations, after unforeseen errors. As part of the development, the restart states in the system are calculated from the model. Upon recovery to simplify the resynchronization between the control system and the physical system, the operator sets the active state of the control system to such a restart state and modifies the physical system accordingly. By recovering from a restart state, it is guaranteed that the system can eventually finish an ongoing emptying procedure.

The truck and the support devices are connected using the Robot Operating System (ROS). ROS is an operating system-like robotics middleware that among other things enables message-passing between components defined in the system. Two types of messages are used in the ROAR project. The first type is messages related to starting and completion of operations. An operation start message is triggered from a user interface and is translated into a method call in the support device executing that operation. Under nominal conditions, this support device will eventually respond with a message saying that the operation has been completed. Both messages will update the current active state of the control system.

The second type is messages related to transferring data. Data transfer can be both internally within the programs connected to a support device and externally between support devices. An example of external data transfer is a path that is created in the path planner and then transferred to the robot.

During execution, the discrete event model is hosted on a web server. Interaction with the model is facilitated by the server’s API. Operator interaction is accomplished through a web based user interface. By enabling a web-based interface an operator can access the model using any device connected to the system’s network. This can for example be a computer in the truck cabin or a touchpad strapped to the operator’s forearm.

At the other end, ROS is also connected to the API. As pointed out before, this connection enables that operations started by the operator through the user interface are translated into method calls in the appropriate support device. Completion of the operation execution is translated into a post-request in the API. This will update the discrete event model to capture that the operation has been completed.
The physical demonstrator in the ROAR project is limited to a single robot for the bin handling. A next step could be to include more bin handling robots. For the specific field of application with refuse handling, more bin handling robots could enable higher efficiency in the emptying procedure. Many robots might also permit that the noisy truck can be parked further away from the bins, and thus cause less disturbance where people live. Today this is to be avoided because a distant truck will force the operator to walk too long.

From a more general point of view, coordination of multiple autonomous devices is an open research question. The two extremes are that the coordination is either performed from one central unit to which all devices are connected, or that the devices are intelligent enough to solve the coordination internally among them in a distributed manner. The two major coordinating challenges to handle is distribution of tasks between the devices and distribution of space where the devices can operate. The overall goal is thus so accomplish all tasks in some optimal way assuring that no devices are physically blocked in the operating area.

The productification of this overall control and coordination between one truck and several autonomous support devices is an interesting challenge. Imagine a future scenario where a haulage contractor company orders a new system. The truck is perhaps ordered from company A, with heavy-duty equipment from company B. The equipment is complemented with support devices from company C and company D. To operate properly, the system should also use services from the cloud, provided by some companies E and F. To further add to the equation, it is likely that operators are also in the loop to cope with unforeseen situations, complex item handling and parts of the decision making.

All in all, this text has only cracked open the door for what will come after the autonomous driving of passenger cars that we see today. There are still many mountains to climb and standards to agree upon before other areas than “just” the driving becomes automated. The outcome of the ROAR project is thus only a small step on a long journey a head.

The ROAR project is initiated and lead by Volvo Group. Chalmers University of Technology, Mälardalen University. Pennsylvania State University take part in the project as being Preferred Academic Partners to Volvo Group. The intention from Volvo Group is that students through bachelor and master theses should perform most of the development.

Article provided by
Patrik Bergagård, PhD, ROAR Project Leader
Martin Fabian, Professor, Automation
IFAC Technical Committee 1.3: Discrete Event and Hybrid Systems