The concept of Smart City is gaining popular attention with the goal of sustainability and efficiency, the needs of enhancing quality and performance, and the explosion of technological advances in communication and computation. Given that 50% of the world’s population lives in urban regions, critical infrastructures of energy, transportation, and health and their growing interdependencies have to be collectively analyzed and designed to provide the substrate for the realization of the Smart City Concept. This talk will address one of these infrastructures, Urban Mobility, and in particular the concept of dynamic toll pricing to alleviate congestion. With the growth and expansion of many large metropolitan centers in the last few decades, the problem of traffic congestion continues to grow and vex commuters, commercial drivers, city planners and officials, and environmentalists worldwide. Over 1 billion vehicles travel on the roads today, and that number is projected to double by 2020. Driving a car is an unavoidable choice for at least 50% of city populations, who rely on their vehicles to get to school or to work. Transactive control, the concept of feedback through economic transactions, appears to be a promising tool for addressing traffic congestion. In particular, we have explored dynamic toll pricing for alleviating traffic congestion and increasing traffic flow during peak hours of the day. A model-based approach to dynamic toll pricing has been developed to provide a systematic method for determining optimal toll pricing schemes. Real-time traffic information from on-road sensors is integrated with complex models of driver behavior and traffic flow to determine the toll price, which acts as a controller to divert traffic flows to desired lanes and routes and lessen the traffic congestion experienced in certain areas. The overall idea of transactive control with particular illustrations of dynamic toll pricing will be presented in this talk.
Distinguished Lecturers Program
The Control Systems Society is continuing to fund a Distinguished Lecture Series.
The primary purpose is to help Society chapters provide interesting and informative programs for the membership, but the Distinguished Lecture Series may also be of interest to industry, universities, and other parties.
The Control Systems Society has agreed to a cost sharing plan which may be used by IEEE Chapters, sections, subsections, and student groups. IEEE student groups are especially encouraged to make use of this opportunity to have excellent speakers at moderate cost.
At the request of a Society Chapter, (or other IEEE groups as mentioned above), a lecture will be scheduled at a place and time that is mutually agreeable to both the Chapter and the Distinguished Lecturer. The Control Systems Society will pay ground transportation at the origin, and Economy Class air fare up to a maximum limit of $1,000 for trips within the same continent and $2,000 for intercontinental trips. The chapter will pay the ground transportation at the destination, hotel, meals, and other incidental expenses. Lecturers will receive no honorarium. Note that the group organizing the lecture must have some IEEE affiliation, the lecture must be free to attend by IEEE members.
When you wish to use this program, you may contact the Distinguished Lecturer directly to work out a tentative itinerary. Then, you must submit a formal proposal to the Distinguished Lecturer Program Chair for his/her approval. The proposal should be sent to the Distinguished Lecturer Program Chair by someone in the local chapter, who should identify their role in the chapter, and provide some details of the invitation, including the dates. The proposal should contain a budgetary quotation for air fare from an authorized source (air line/ travel agent), and a confirmation that the local chapter will pay their share of the expenses associated with the trip. If the trip is approved, then IEEE CSS would pay ground transportation at the origin, and Economy Class air fare up to a maximum limit of $1,000 for trips within the same continent and $2,000 for intercontinental trips. The chapter will pay the ground transportation at the destination, hotel, meals, and other incidental expenses. Procedures for unusual situations (such as when the speaker has other business on the trip) should be cleared through the Distinguished Lecturer Program Chair.
The expense claim filed by the distinguished lecturer upon the conclusion of the trip should contain receipts for the airfare and ground transportation at the origin.
Each distinguished lecturer will be limited to two trips per year, out of which at most one can be inter-continental.
Distinguished Lecturers Program Chair
Two major players in a smart grid are renewables and flexible consumption. The former is necessitated by global concerns of sustainability and greenhouse gas emissions, and dwindling resources of fossil fuels. The latter is enabled through the feasibility of fast and large-scale communication and the growing acceptance and economic potential of flexible consumption. Introduction of these two players brings with it a host of challenges, many of which stem from the introduction of complex and uncertain dynamics at various time-scales. In order to assess the impact of these dynamics, and realize the desired goals of a smart grid, of delivering affordable and reliable power to all end-users, an end-to-end framework that is dynamic, and allows the deployment of various analysis and synthesis tools of stability, estimation, optimization, and control is needed. This framework should not only encompass the physically relevant, and traditional timescales of frequency and voltage control, but economically relevant market-based decisions for planning and economic dispatch. More importantly, this framework should address the interactions between the former active-control components that manipulate physical variables and the latter transactive-control components that manipulate economic variables. In this talk, recent results developed in the AAC laboratory at MIT related to the development of such a dynamic framework will be presented.
Adaptive Control is viewed as a game changer in many application domains where real-time feedback control is essential to ensure the desired performance. Adaptive controllers, whose distinguishing feature is a parameter estimator that prescribes the rule for changing the control parameters in real-time, have been studied extensively over the past forty years, with fundamental properties of stability and robustness well understood. Guidelines for analysis and synthesis for adaptive controllers have been laid out for linear and (specific classes of) nonlinear systems, continuous and discrete-time systems, single-input and multi-input systems, and deterministic and stochastic systems. So what’s missing? There are glaring gaps in adaptive control theory that remain to be closed for adaptive control to be a viable, practical, and easily implementable methodology. Guarantees have to be provided that ensure robustness to a wide variety of non-parametric perturbations. Guidelines have to be in place for a systematic design of all free parameters in the controller. Bounds have to be derived, not only for steady-state behavior, but also for transient characteristics. Implementation issues will have to be satisfactorily addressed. The ability to accommodate actuator constraints in terms of bandwidth, magnitude limits, and rate limits has to be precisely characterized. Recently, there have been breakthroughs in Adaptive Control that have led to reducing the above gaps. This talk will outline the basic principles of the now classical adaptive control theory, but also highlight these recent results and show how they contribute towards making adaptive control practical.
A major problem for today’s large-scale networked systems is to certify the required stability, performance, and safety properties using analytical and computational models. The existing methods for such certification are severely limited in their ability to cope with the number of physical components and the complexity of their interactions We address this problem with a compositional approach that derives network-level guarantees from key structural properties of the subsystems and their interactions, rather than tackle the system model as a whole. The foundational tool in our approach is the established dissipativity theory, enriched with modern computational techniques. Dissipativity properties serve as abstractions of the detailed dynamical models of the subsystems and allow us to decompose intractably large certification problems into subproblems of manageable size. We leverage large-scale optimization techniques to detect useful dissipativity properties and exploit interconnection symmetries for further computational savings. Case studies demonstrate the applicability of the methods to biological networks, vehicle platoons, and Internet congestion control.
We present a formal methods approach to meet temporal logic specifications in traffic control. Formal methods is an area of computer science that develops efficient techniques for proving the correct operation of systems, such as computer programs and digital circuits, and for designing systems that are correct by construction. We highlight key structural properties of traffic networks that make them amenable to this approach. The first structural property is “mixed monotonicity” which relaxes the classical notion of an order-preserving (“monotone”) system. We discuss how this property allows a computationally efficient finite abstraction and illustrate the result on a macroscopic model of traffic flow in a road network. The second structural property is decomposability into sparsely connected sub-networks. Using this property, we exhibit a compositional synthesis technique that introduces supply and demand contracts between the subsystems and ensures the soundness of the composite controller.
Breaking symmetry in spatially distributed networks is a fascinating dynamical systems problem and is of fundamental interest to developmental biology. We discuss two types of local interaction that underlie formation of gene expression patterns in multi-cellular organisms: diffusion and cell-to-cell contact signaling. We first present new insights on a diffusion-driven mechanism for pattern formation and propose a synthetic gene network built upon this mechanism. We then discuss contact-mediated inhibition that is responsible for segmentation and fate-specification. We introduce a dynamical model to represent this mechanism and reveal the key properties of the model that are necessary for pattern formation. The results also yield new insights for the converse problem of maintaining spatial homogeneity, that is, synchrony. We conclude the talk with a distinct biological problem where synchronization plays an important role: the locomotion of swimming microorganisms. Examples include the bundling of flagella and coordination of cilia. With large-scale numerical simulation results for low Reynolds number flows, we argue that synchronization can result from hydrodynamic interactions alone.
Network systems are mathematical models for the study of cooperation,
propagation, synchronization and other dynamical phenomena that arise among
interconnected agents. Network systems are widespread in science as they
are fundamental modeling tools, e.g., in sociology, ecology, and
epidemiology. They also play a key growing role in technology, e.g., in the
design of power grids, cooperative robotic behaviors and distributed
computing algorithms. Their study pervades applied mathematics.
This talk will review established and emerging frameworks for modeling,
analysis and design of network systems. I will survey the available
comprehensive theory for linear network systems and then highlight selected
nonlinear concepts. Next, I will focus on recent developments by my group
on (i) modeling of the evolution of opinions and social power in social
networks, (ii) analysis of security and transmission capacity in power
grids, and (iii) design of optimal strategies for robotic routing and
Control of complex networks, including unmanned vehicle networks, social networks, and biological systems, is an ever-growing challenge. A standard approach is to directly control a subset of leader nodes, which then influence the remaining (follower) nodes. While the choice of leader nodes is known to impact the performance, controllability, and security of complex networks, efficient algorithms for selecting optimal leaders are currently lacking.In this talk, we give an overview of our ongoing work on leader selection in complex networks. We focus on three design criteria, namely, the robustness of the system to noise in the links between nodes, the time for the follower nodes to converge to their desired state, and the controllability to the follower nodes from the leader nodes. We present a unifying framework based on submodularity, a diminishing returns property analogous to concavity of real-valued functions, for studying each of these criteria. Our framework enables efficient leader selection based on the criteria above, with provable guarantees on the resulting system performance. Moreover, we generalize our approach to time-varying networks, including networks with random failures, arbitrary topology variations due to node mobility, and attacks by an intelligent adversary targeting one or more links.
Scenario optimization is a general methodology that enables one to make designs based on knowledge sourced from empirical data. When the scenario design is applied to a new case, its performance is guaranteed by the generalization theory that underpins the method. In this talk, the scenario approach will be presented along with its theoretical foundations. The generality of the scenario approach makes it useful across a variety of fields including control, identification and classification and examples will be provided to highlight its versatility.
Classical theories of system identification are grounded on probabilistic assumptions under which various methods are guaranteed to converge, to be asymptotically efficient, etc. In this talk, we shall contend that theoretical guarantees can be obtained under way less assumptions than traditional theories do and shall make a case for the need to spend more research effort in this direction. This suggests a paradigm shift where prior knowledge only impacts on visible characteristics of the model, such as the extension of the identified region or the width of an interval used for prediction, while the model reliability is guaranteed under minimal a priori assumptions.
Virtual Reference Feedback Tuning (VRFT) is a method to design controllers based on empirical data. A reference model is assigned by the user and the method automatically designs the best possible controller according to a 2-norm metric within the considered controller class. This is obtained by recasting the original non-convex controller design as a convex design amenable of implementation by means of a set of input-output measurements obtained from the plant. In this talk, I shall present the foundations of VRFT and shall illustrate it through application studies.
It is well-known that feedback can be introduced to stabilize an unstable system, to attenuate the response of a system to disturbance, and to reduce the effect of plant parameter variations and modeling error. On the other hand, feedback design is also known to be contingent on various performance considerations and physical constraints, which invariably impose limitations on the achievable performance and necessitate tradeoffs among conflicting design objectives. An important step in the feedback design process, therefore, is to analyze how system properties may inherently impose constraints upon design and thus may fundamentally limit the performance attainable. In this talk I shall present a control theorist’s perspective into this intriguing area of scientific inquiry, from the early triumph of feedback theory to the latest development in networked control. The talk will begin with a tutorial review of Bode's classical integral relations, widely considered a pillar of feedback theory. This will then usher in the more recent progress, of which multivariable integral relations of Bode and Poisson type, and a number of canonical optimal control problems will constitute the primary theme. Interpretations of these results from control perspectives will be particularly emphasized. The talk will focus on multivariable systems and address a number of new, unique issues only found in multivariable systems, with a particular undertone to networked control systems.
Abstract: Neuromuscular Electrical Stimulation (NMES) is prescribed by clinicians to aid in the recovery of strength, size, and function of human skeletal muscles to obtain physiological and functional benefits for impaired individuals. The two primary applications of NMES include: 1) rehabilitation of skeletal muscle size and function via plastic changes in the neuromuscular system, and 2) activation of muscle to elicit movements that result in functional performance (i.e., standing, stepping, reaching, etc.) termed functional electrical stimulation (FES). In both applications, stimulation protocols of appropriate duration and intensity are critical for preferential results. Automated NMES methods hold the potential to maximize the treatment by self-adjusting to the particular individual (facilitating potential in-home use and enabling positive therapeutic outcomes from less experienced clinicians). Yet, the development of automated NMES devices is complicated by the uncertain nonlinear musculoskeletal response to stimulation, including difficult to model disturbances such as fatigue. Unfortunately, NMES dosage (i.e., number of contractions, intensity of contractions) is limited by the onset of fatigue and poor muscle response during fatigue. This talk describes recent advances and experimental outcomes of control methods that seek to compensate for the uncertain nonlinear muscle response to electrical stimulation due to physiological variations, fatigue, and delays.
Analytical solutions to the infinite horizon optimal control problem for continuous time nonlinear systems are generally not possible because they involve solving a nonlinear partial differential equation. Another challenge is that the optimal controller includes exact knowledge of the system dynamics. Motivated by these issues, researchers have recently used reinforcement learning methods that involve an actor and a critic to yield a forward-in-time approximate optimal control design. Methods that also seek to compensate for uncertain dynamics exploit some form of persistence of excitation assumption to yield parameter identification. However, in the adaptive dynamic programming context, this is impossible to verify a priori, and as a result researchers generally add an ad hoc probing signal to the controller that degrades the transient performance of the system. This presentation describes a forward-in-time dynamic programming approach that exploits the use of concurrent learning tools where the adaptive update laws are driven by current state information and recorded state information to yield approximate optimal control solutions without the need for ad hoc probing. A unique desired goal sampling method is also introduced as a means to address the classical exploration versus exploitation conundrum. Applications are presented for autonomous systems including robot manipulators, underwater vehicles, and fin controlled cruise missiles. Solutions are also developed for networks of systems where the problem is cast as a differential game where a Nash equilibrium is sought.
Passivity concepts have been a topic of interest widely in systems and control. In particular, they have provided unified fundamental tools for a variety of robot control problems. In this talk, I shall describe new developments in passivity-based control in robotics; namely in cooperative control of robotic networks and in visual feedback with visual motion observer. First the talk begins with output synchronization for networked robotics, consisting of nonlinear passive dynamics and of rigid body networks on SE(3). Then it focuses on systematic construction of visual motion observer for three-dimensional dynamic motion estimation, which enables us to synthesize visual feedback control. By exploiting passivity concepts further, an emerging topic of human robotic-networks teaming is also examined and discussed. Rich experimental case studies with hands-on robotic testbeds are effectively demonstrated throughout the talk.
An initiative for Smart Cities has been promoted worldwide as societal-scale CPS (Cyber-Physical Systems) infrastructures. Along with efficient traffic/water/security management, distributed EMS (Energy Management Systems) should play a key role as we head toward low carbon environmental friendly society that is essential for sustainable development. To this goal, JST (Japan Science and Technology Agency) has launched a CREST research area for the distributed EMS building. The aim of this project is to create fundamental theory and advanced technology for optimal control of energy balancing between dynamic demand and supply. The topics covered include forecast and integration of renewable energy, management of electric vehicle/storage, demand response and human behavior, and platform building. A particular emphasis is on the promotion of international research collaboration with the US and European Funding Agencies. This would enable all the researchers involved to catalyze networking and knowledge sharing with a broad array of disciplines. In this talk, the on-going exciting progress of the CREST EMS project is presented.
Freight transportation is of outmost importance for the development of our society and economy. At the same time, transporting goods on roads accounts for a significant amount of all energy consumption and greenhouse gas emissions. Despite this influence, road transportation is mainly done today by individual long-haulage trucks with no real-time coordination or global optimization. In this lecture, we will discuss how modern information and communication technology supports a cyber-physical transportation system architecture with an integrated logistic system coordinating fleets of trucks traveling together in vehicle platoons. From the reduced air drag, platooning trucks traveling close together can save more than 10% of their fuel consumption. Control and estimation challenges and solutions on various level of this transportation system will be presented. It will be argued that a system architecture utilizing vehicle-to-vehicle and vehicle-to-infrastructure communication enables safe and optimal control of individual trucks as well as optimized vehicle fleet collaborations. Empirical evidence will be presented for why large-scale fleet coordination is mainly a scheduling (not a routing) problem. Incentives for cooperation and pricing of transport services will also be discussed. Several experiments done on European highways will illustrate achievable system performance and potential obstacles to be overcome. The presentation will be based on joint work with collaborators at KTH and at the truck manufacturer Scania.
Cyber-attacks on critical infrastructures are of growing societal concern. Several malicious attacks have been reported over the last few years and in many cases they have targeted control systems. The increasing use of off-the-shelf software and hardware components and open communication networks makes networked control systems vulnerable to cyber-attacks. As the cyber and physical components of these systems are tightly interconnected, traditional IT security focusing on the cyber part does not provide appropriate solutions. In this talk, we will discuss how to model, analyze and design cyber-secure networked control systems. We will introduce an adversary modeling framework and use it for quantifying cyber-security of control systems by means of constrained optimization problems. An attack space defined by the adversary's model knowledge, disclosure, and disruption resources is presented. It is shown that attack scenarios corresponding to denial-of-service, replay, zero-dynamics, and bias injection attacks can be analyzed using this framework. Applications to power networks and process industry will be used to illustrate the attack scenarios, their consequences, and potential countermeasures.
There is a growing deployment of wireless networks in industrial control systems. Lower installation costs and efficient system reconfigurations for wireless devices have a major influence on the future application of distributed control. Traditional sampled-data control is based on periodic sensing and actuation rather than the acting when the system needs attention. Event-based control instead is reactive and generates sensor sampling and control actuation when the plant needs it. In this talk, we will discuss how to design event-based control systems. It will be shown how wireless access scheme for can influence the closed-loop performance of the networked control system. It will be argued that the underlying scheduling control problem has a non-classical information structure. Appropriate models for medium access control protocols will be introduced. It will be shown how these protocols can be tuned for various wireless control applications. The talk will be illustrated by several examples from ongoing projects with Swedish industry. The presentation is based on joint work with several collaborators.
In many application domains, Simulink/Stateflow serves as a platform for model-based development of the reactive embedded code, that interacts with its environment in real-time fashion. The talk will present a model-based approach for testing Simulink/Stateflow code, based on its automated translation to input-output extended finite automaton (I/O-EFA), followed by automated test-generation, guaranteeing user-defined code as well as requirements coverage, and also support for automated test-execution and error-localization. While testing is useful for design-time error analysis, the talk will further discuss our model-based approach for run-time error monitoring, detection and localization. Monitoring at system level (as opposed to software level) is necessarily stochastic, and a more general I/O-Stochastic Hybrid Automaton (I/O-SHA) model is used, and condition is obtained for bounded-delay detectability, and achieving desired levels of false-positives/-negatives.
Population growth, urbanisation and climate change necessitate a paradigm shift in the design and operations of the classical electrical power grid. The original ideas underpinning the first AC grids of the late 19th century still define the present grid, which consists of large power sources at a few distinct locations supplying through the high voltage transmission grid a large, geographically distributed low voltage consumer base. Much of this paradigm is being questioned at present because
a) Renewable power sources come with a far lower power intensity per square meter of installation;
b) Renewable power sources suffer from uncontrollable temporal variations unknown in classical power generation;
c) In well-established grids, peak-to-base power consumption is increasing, making the transmission grid which caters by necessity for peak demand an economically very unattractive proposition.
At the same time, new technologies provide opportunities
a) smart metering, but more generally intelligent, interconnected, infrastructure or an internet-of-things for the grid, is totally feasible;
b) transport is becoming more electrified, with electric vehicles entering the light vehicle market;
c) electrical energy storage, or non-fossil fuel energy storage at scale is becoming an economically realistic proposition.
In particular these new technologies allow us to reconsider what the last mile in the grid may look like when demand and supply are coordinated through a power matching strategy that respects the physical infrastructure's operational limits. We argue the economic need to consider such approaches in the distribution grid, based on grid usage considerations. Distributed, receding horizon optimized distribution of power to satisfy consumers' energy needs, minimize their energy bills, whilst maximising the utility of renewables, and the grid itself is a realistic option that may change the way we use electrical power and build and exploit distribution networks.
Much of our experience, and the data used in the presentation, are Australia specific. Nevertheless, we will consider scenarios applicable to both high population density urban living as well as semi-rural, and rural circumstances, inclusive of some remarks around the management of micro-grids that may evolve as demand requires.
The talk will conclude with some observations about the socio-economic and political dimensions of a grid infrastructure supplied by renewable power sources. Non-trivial national regulatory reform is required in Australia, but such reform is insignificant when compared with the trans-national and trans-regional cooperation that is essential to achieve equitable world-wide access to renewable power.
Recent years have witnessed significant interest in the area of multi-agent or networked control systems, with applications ranging from autonomous vehicle teams to communication networks to smart grid energy systems. The setup is a collection of decision-making components with local information and limited communication interacting to balance a collective objective with local incentives. While game theory is well known for its traditional role as a modeling framework in social sciences, it is seeing growing interest as a design approach for distributed control. Of particular interest is game theoretic learning, in which the focus shifts away from equilibrium solution concepts and towards the dynamics of how decision makers reach equilibrium. This talk presents a tutorial overview of game theoretic learning, from its origins as a "descriptive" tool for social systems to its "prescriptive" role as an approach to design on linear learning algorithms for distributed architecture control. The talk presents a sampling of prior and recent results in these areas along with several illustrative examples of distributed coordination.
Solution concepts in game theory, such as Nash equilibrium, traditionally ignore the processes and associated computational costs of how agents go about deriving strategies. The notion of bounded rationality seeks to address such issues through a variety of alternative formulations. This talk presents two settings motivated by bounded rationality. First, we consider incomplete information dynamic games. A Nash equilibrium in this setting requires each agent to solve a partially observed Markov decision problem that requires knowledge of a possibly extensive environment as well as the strategies of other agents. We introduce an alternative notion, called “empirical evidence equilibria”, in which agents form naive models with available measurements. These models reflect an agent’s limited awareness of its surroundings, and the level of naivety or sophistication can be different for each agent. We show that such equilibria are guaranteed to exist for any profile of agent rationality and compare the concept to mean field equilibria. Second, we investigate learning in evolutionary games, where the focus is on the dynamic behaviors away from equilibrium rather than characterizations of equilibrium. A lingering issue in this framework is what constitutes “natural” versus “concocted” learning rules. Building on prior work on so-called “stable games”, we introduce a class of dynamics motivated by control theoretic passivity theory. We show how passivity theory both captures and extends selected prior work on evolutionary games and offers a candidate for what constitutes natural learning.
Power systems are going through a paradigm change from centralized generation, to distributed generation, and further on to smart grids. In order to make power systems more secure, more efficient, more resilient to threats and friendlier to the environment, a huge number of heterogeneous players, including renewable energy sources, electric vehicles, and storage systems etc. on the supply side and different types of smart loads on the demand side, are being connected to power systems to form smart grids. Because of the heterogeneous nature and the huge number of players involved, it is a great challenge to find a system architecture so that all heterogeneous players could work together to maintain system stability and achieve desired performance. In this talk, an autonomous distributed control architecture will be presented from the systems perspective for the next-generation smart grid, after homogenizing the heterogeneous players with the synchronization mechanism of synchronous machines. Two technical routes will be presented to implement this architecture: one is based on the synchronverter technology that makes power converters behave like synchronous machines and the other is based on the robust droop control technology that mimics the external function of synchronous machines. All the distributed controllers require only the information available locally and communicate with each other through the dynamics of power systems, rather than through an additional communication network. They equally and actively take part in the system regulation via independent individual actions to achieve the same control objective, in the same way as conventional power plants do. This holistic solution could considerably enhance the stability, scalability, operability and reliability of the next-generation smart grid.