Distinguished Lecturers Program

Program Description

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

Distinguished Lecturers

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Distinguished Lecturer

Compositional certification of stability, performance, and safety for interconnected systems

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. 


Formal synthesis for traffic 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. 


Pattern formation and synchronization in biology

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.


John Baillieul Headshot Photo
Distinguished Lecturer

Information Gradients and Topology in Random Fields

Using concepts from differential topology and information theory, I shall describe a theoretical framework for search strategies aimed at rapid discovery of topological features (locations of critical points and critical level sets) of a priori unknown differentiable random fields. The theory enables study of efficient reconnaissance strategies in which the tradeoff between speed and accuracy can be understood. The proposed approach to rapid discovery of topological features has led in a natural way to to the creation of parsimonious reconnaissance routines that do not rely on any prior knowledge of the environment. The design of topology-guided search protocols uses a mathematical framework that quantifies the relationship between what has been discovered and what remains to be discovered. The quantification rests on an information theory inspired model whose properties allow us to treat search as a problem in optimal information acquisition.

Information Based Control and Control Communication Complexity

The interaction of information and control has been a topic of interest to system theorists that can be traced back to the 1950’s when the fields of communications, control, and information theory were new but developing rapidly.  Recent advances in our understanding of this interplay have emerged from work on the dynamical effect of state quantization and a corresponding understanding of how communication channel data rates affect system stability.  While a large body of research has now emerged dealing with communication constrained feedback channels and optimal design of information flows in networks, less attention has been paid to ways in which control systems should be designed in order to optimally mediate computation and communication.  Such optimization problems are of interest in the context of quantum computing, and similar problems have recently been discussed in connection with protocols for assembly of molecular components in synthetic biology.

The Standard Parts Problem and Quantization in Optimal Control

Recently W.S. Wong has proposed the concept of control communication complexity (CCC) as a formal approach for understanding how  a group of distributed agents can choose independent actions from a prescribed "action code book" that cooperatively realize common goals and objectives.  A prototypical goal is the computation of a function, and CCC provides a promising new approach to understanding complexity in terms of the cost of realizing a selected evaluation.  This lecture will introduce control communication complexity in terms of what are called standard parts optimal control problems. Problems in optimal ensemble averaged motion sequences and distributed control of dynamical systems defined on Lie groups are discussed.

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Distinguished Lecturer

Network Systems in Science and Technology

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


Linda Bushnell Headshot Photo
Distinguished Lecturer

Leader Title: Leader Selection for Performance and Control of Complex Networks

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.  

Submodularity in Dynamics and Control of Networked Systems
Networked systems, consisting of distributed nodes that sense their surroundings, exchange information with other nodes, and perform actuation, play an ever-increasing role in applications such as transportation, energy, and health care. In order to provide guarantees on stability and performance, these systems must be controlled via external inputs. An efficient way to do this is by controlling a small subset (leaders) of the network nodes, which then steer the “follower nodes” to the desired state via local interactions. The choice of input nodes will determine critical properties of the system, such as robustness, controllability, and convergence. Selecting a subset of input nodes, however, is inherently a discrete optimization problem, making continuous optimization techniques for control synthesis inapplicable. This talk will describe a submodular optimization framework for selecting the input nodes. Submodularity is a diminishing returns property of discrete functions, analogous to concavity of continuous functions that enables efficient optimization algorithms with provable optimality guarantees. 

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Distinguished Lecturer

Scenario-optimization: a methodology for control, identification and classification

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.


Distribution-free results in system identification

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.


Data-based controller design: the virtual reference approach

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.


Jie Chen Headshot Photo
Distinguished Lecturer

Control Performance Limitation: A Revisit in the Information Age

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.

When is a time-delay system stable and stabilizable?
A time-delay system may or may not be stable for different lengths of delay, and further, may or may not be stabilized via feedback. When will then a delay system be stable or unstable, and for what intervals of delay? What will be the largest range of delay that a feedback system can tolerate? Fundamental questions of this kind have long eluded engineers and mathematicians alike, yet ceaselessly invite new thoughts and solutions. In this talk I shall present an analytical tool that answers to these questions, seeking to provide exact and efficient computational solutions to stability and stabilization problems of time-delay systems. The approach consists of the development of eigenvalue perturbation series and intrinsic delay bounds for stabilization. The former seeks to characterize the analytical and asymptotic properties of eigenvalues of matrix functions or operators. When applied to stability problems, the essential issue dwells on the asymptotic behavior of the critical eigenvalues on the imaginary axis, that is, on how the imaginary eigenvalues may vary with respect to the varying parameter. This behavior determines whether the imaginary eigenvalues cross from one half plane into another, and hence plays a critical role in determining the stability of such systems. The latter characterizes analytically the largest range of delay for which a system can be stabilized by a feedback controller. These results depart from the currently pervasive, typically LMI conditions, and yet are conceptually appealing and computationally efficient, requiring only the solution of a generalized eigenvalue problem.  

Warren Dixon Headshot Photo
Distinguished Lecturer

Theoretical and Experimental Outcomes of Closed-Loop Neuromuscular Control Methods to Yield Human Limb Motion

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.

Concurrent Learning-Based Adaptive Dynamic Programming for Autonomous Agents

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.  

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Distinguished Lecturer

Model-Based Testing and Monitoring for Embedded Software

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. 

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Distinguished Lecturer

Renewable Energy Based Grid Futures - A View from the Last Mile

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.


Qing-Chang Zhong  Headshot Photo
Distinguished Lecturer

Autonomous Distributed Control of the Next-Generation Smart Grid

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.