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Category: Sustainable control

Closed-loop weight control: the power of feedback

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.


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

The approach suggested in this article relies on the following assumptions:

  • 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.


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

Control of Dynamical Systems by Partial Differential Equations


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),
  • 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.


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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

A survey on industry impact and challenges thereof


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%
11. Adaptive control 17% 43%
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.

Statement Agreement Disagreement Academia/Industry
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.


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!

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

Consumer-driven automation for smartening the grid


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.


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

“Sustainable” control of offshore wind turbines

The motivation for this issue comes from a real need to have an open discussion about the challenges of control for very demanding systems, such as wind turbine installations, requiring the so-called “sustainability” features. It represents the characteristic to tolerate possible malfunctions affecting the system and, at the same time, the capability to continue working while maintaining power conversion efficiency. Sustainable control has  begun to stimulate research and development in a wide range of industrial communities particularly for those systems demanding a high degree of reliability and availability. The system should be able to maintain specified operable and committable conditions, and at the same time should avoid expensive maintenance works. For offshore wind farms a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance.

Renewable energy can be produced from a wide variety of sources including wind, solar, hydro, tidal, geothermal, and biomass. By using renewables in a more efficient way to meet its energy needs, the EU lowers its dependence on imported fossil fuels and makes its energy production more sustainable and effective. The renewable energy industry also drives technological innovation and employment across Europe, as highlighted for the wind power conversion systems.

2020 renewable energy targets are settled. The EU’s Renewable Energy Directive sets a binding target of 20% final energy consumption from renewable sources by 2020. To achieve this, EU countries have committed to reaching their own national renewables targets ranging from 10% in Malta to 49% in Sweden. They are also each required to have at least 10% of their transport fuels come from renewable sources by 2020 [1]. All EU countries have adopted national renewable energy action plans showing what actions they intend to take to meet their renewables targets. These plans include sectorial targets for electricity, heating and cooling, and transport; planned policy measures; the different mix of renewables technologies they expect to employ; and the planned use of cooperation mechanisms.

A new target for 2030 is fixed. Renewables will continue to play a key role in helping the EU meet its energy needs beyond 2020. EU countries have already agreed on a new renewable energy target of at least 27% of final energy consumption in the EU as a whole by 2030. This target is part of the EU’s energy and climate goals.

Support schemes for renewables are available, which drive the technological innovation and employment in this framework. Horizon 2020 is the biggest EU Research and Innovation programme ever with nearly €80 billion of funding available over 7 years (2014 to 2020) – in addition to the private investment that this money will attract. It promises more breakthroughs, discoveries and world-firsts by taking great ideas from the lab to the market. Horizon 2020 is the financial instrument implementing the Innovation Union, a Europe 2020 flagship initiative aimed at securing Europe’s global competitiveness [1].

By coupling research and innovation, Horizon 2020 is helping to achieve this with its emphasis on excellent science, industrial leadership and tackling societal challenges. The goal is to ensure Europe produces world-class science, removes barriers to innovation and makes it easier for the public and private sectors to work together in delivering innovation.

Wind energy is perhaps the most advanced of the ‘new’ renewable energy technologies, but there is still much work to be done. Assessments of the research and technology developments and impacts have been highlighted by recent proposals within the Horizon 2020, with key benefits from both the scientific and industrial points of view.

Wind energy can be considered as a fast–developing multidisciplinary field consisting of several branches of engineering sciences. The National Renewable Energy Laboratory estimated a growth rate of the wind energy installed capacity of about 30% from 2001 to 2006, and even with a faster rate up to 2014.

The global wind power installations are 369,6 GW in 2014, with an expected growth to 415.7 GW by the end of 2015. After 2009, more than 50% of new wind power resources were increased outside of the original markets of Europe and U.S., mainly motivated by the market growth in China, which now has 101,424 MW of wind power installed. Several other countries have obtained quite high levels of stationary wind power production, with rates from 9% to 39%, such as in Denmark, Portugal, Spain, France, Ireland, Germany, Ireland, and Sweden in 2015. From 2009, 83 countries around the world are exploiting wind energy on a commercial basis, as wind power is considered as a renewable, sustainable and green solution for energy harvesting. Note however that, even if the U.S. now achieves less than 2% of its required electrical energy from wind, the most recent National Renewable Energy Laboratory’s report states that the U.S. will increase it up to 30% by the year 2030. Note also that, even if the fast growth of the wind turbine installed capacity of wind turbines in recent years, multidisciplinary engineering and science challenges still exist. Moreover, wind turbine installations must guarantee both power capture and economical advantages, thus motivating the wind turbine dramatic growth [1].

Industrial wind turbines have large rotors and flexible load–carrying structures that operate in uncertain, noisy and harsh environments, thus motivating challenging cases for advanced control solutions [2].  Advanced controllers can be able to achieve the required goal of decreasing the wind energy cost by increasing the capture efficacy; at the same time they should reduce the structural loads, thus increasing the lifetimes of the components and turbine structures.

Although wind turbines can be developed in both vertical–axis and horizontal–axis configurations, the industrial and technological interest focusses on horizontal–axis wind turbines, which represent the most commonly solutions today in the produced large–scale installations. Horizontal–axis wind turbines have the advantage that the rotor is placed atop a tall tower, with the advantage of larger wind speeds that the ground. Moreover, they can include pitchable blades (i.e. they can be oriented with respect to the wind direction) in order to improve the power capture, the structural performance, and the overall system stability. On the other hand, vertical–axis wind turbines are more common for smaller installations. Note that proper wind turbine models are usually oriented to the design of suitable control strategies that are more effective for large rotor wind turbines. Therefore, the most recent research focus considers wind turbines with capacities of more than 10 MW [3].

Another important issue derives from the increasing complexity of wind turbines, which gives rise to more strict requirements in terms of safety, reliability and availability. In fact, due to the increased system complexity and redundancy, large wind turbines are prone to unexpected malfunctions or alterations of the nominal working conditions. Many of these anomalies, even if not critical, often lead to turbine shutdowns, again for safety reasons. Especially in offshore wind turbines, this may result in a substantially reduced availability, because rough weather conditions may prevent the prompt replacement of the damaged system parts. The need for reliability and availability that guarantees the continuous energy production thus requires sustainable control solutions [2].

These schemes should be able to keep the turbine in operation in the presence of anomalous situations, perhaps with reduced performance, while managing the maintenance operations. Apart from increasing availability and reducing turbine downtimes, sustainable control schemes might also obviate the need for more hardware redundancy, if virtual sensors could replace redundant hardware sensors. These schemes currently employed in wind turbines are typically on the level of the supervisory control, where commonly used strategies include sensor comparison, model comparison and thresholding tests. These strategies enable safe turbine operations, which involve shutdowns in case of critical situations, but they are not able to actively counteract anomalous working conditions. Therefore, recent research directions have been oriented to investigate these sustainable control strategies, which allow to obtain a system behaviour that is close to the nominal situation in presence of unpermitted deviations of any characteristic properties or system parameters from standard conditions (i.e. a fault). Moreover, these schemes should provide the reconstruction of the equivalent unknown input that represents the effect of a fault, thus achieving the so–called Fault Detection and Diagnosis tasks [3].

The need of advanced control solutions for these safety–critical and very demanding systems, motivated also the requirement of reliability, availability, and maintainability over the required power conversion efficiency. Therefore, these issues have begun to stimulate research and development of sustainable control (i.e. fault–tolerant control), in particular for wind turbine applications. Particularly important for offshore installations, Operation and Maintenance (O & M) services have to be minimised, since they represent one of the main factors of the energy cost. The capital cost, as well as the wind turbine foundation and installation determine the basic term in the cost of the produced energy, which constitute the energy “fixed cost”. The O & M represent a “variable cost” that can increase the energy cost up to about 30%. At the same time, industrial systems have become more complex and expensive, with less tolerance for performance degradation, productivity decrease and safety hazards. This leads also to an ever increasing requirement on reliability and safety of control systems subjected to process abnormalities and component faults [2, 3].

As a result, the Fault Detection and Diagnosis tasks, as well as the achievement of fault tolerant features for minimising possible performance degradation and avoiding dangerous situations are extremely important. With the advent of computerised control, communication networks and information techniques, it becomes possible to develop novel real–time monitoring and fault–tolerant design techniques for industrial processes, but this also brings challenges. Several works have been proposed recently on wind turbine Fault Detection and Diagnosis, and the sustainable (fault tolerant) control problem has been recently considered with reference to offshore wind turbine benchmarks, which motivated this issue [3, 4].

In this way, by enabling this clean renewable energy source to provide and reliably meet the world’s electricity needs, the tremendous challenge of solving the world’s energy requirements in the future will be finally enhanced. The wind resource available worldwide is large, and much of the world’s future electrical energy needs can be provided by wind energy alone if the technological barriers are overcome. The application of sustainable controls for wind energy systems is still in its infancy, and there are many fundamental and applied issues that can be addressed by the systems and control community to significantly improve the efficiency, operation, and lifetimes of wind turbines.

[1] Global Wind Energy Council. Wind Energy Statistics 2014. Report, 2014.

[2] Blanke, M.; Kinnaert, M.; Lunze, J.; Staroswiecki, M. Diagnosis and Fault–Tolerant Control; Springer–Verlag: Berlin, Germany, 2006.

[3] Simani S. Overview of Modelling and Advanced Control Strategies for Wind Turbine Systems. Energies, October 2015, 10, 12116–12141. (This article belongs to the Special Issue Wind Turbine 2015)

[4] Odgaard, P.F.; Stoustrup, J. A Benchmark Evaluation of Fault Tolerant Wind Turbine Control Concepts. IEEE Transactions on Control Systems Technology 2015, 23, 1221–1228.

Article provided by
Dr. Silvio Simani 
University of Ferrara
IFAC Technical Committee 6.4 – Safeprocess

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