IEEE.org | IEEE Xplore Digital Library | IEEE Standards | IEEE Spectrum | More Sites
Call for Award Nominations
More Info
General anesthesia is a drug-induced, reversible condition comprised of five behavioral states: unconsciousness, amnesia (loss of memory), analgesia (loss of pain sensation), akinesia (immobility), and maintenance of physiological stability and control of the stress response. As a consequence, every time an anesthesiologist administers anesthesia he/she creates a control system with a human in the loop. Our work shows that a primary mechanism through which anesthetics create these altered states of arousal is by initiating and maintaining highly structured oscillations. These oscillations impair communication among brain regions. We show how these dynamics change systematically with different anesthetic classes, anesthetic dose and with age. As a consequence, we have developed a principled, neuroscience-based paradigm for using the EEG to monitor the brain states of patients receiving general anesthesia and for implementing formal control strategies for maintaining anesthetic state. We will illustrate these strategies with results from actual control experiments.
With the increasing trend towards system downsizing and the growing stringency of requirements, constraint handling and limit protection are becoming increasingly important for engineered systems. Constraints can reflect actuator limits, safety requirements (e.g., process temperatures and pressures must not exceed safe values) or obstacle avoidance requirements. Reference governors are control schemes that can be augmented to already existing control systems in order to provide constraint handling/limit protection capabilities. These add-on schemes exploit prediction and optimization or invariance/strong returnability properties to supervise and minimally modify operator (e.g., pilot or driver) commands, or other closed-loop signals, whenever there is a danger of future constraint violations. The presentation will introduce the basic reference governor schemes along with the existing theory. Several recent extensions and new variants of these schemes will be highlighted. Selected aerospace and automotive applications will be described. Opportunities for future research will be mentioned.
High-gain observers play an important role in the design of feedback control for nonlinear systems. This lecture overviews the essentials of this technique. A motivating example is used to illustrate the main features of high-gain observers, with emphasis on the peaking phenomenon and the role of control saturation in dealing with it. The use of the observer in feedback control is discussed and a nonlinear separation principle is presented. The use of an extended high-gain observer as a disturbance estimator is covered. Challenges in implementing high-gain observers are discussed, with the effect of measurement noise as the most serious one. Techniques to cope with measurement noise are presented. The lecture ends by listing examples of experimental testing of high-gain observers.
In this talk we address the problem of designing nonlinear observers that possess robustness to output measurement errors. To this end, we introduce a novel concept of quasi-Disturbance-to-Error Stable (qDES) observer. In essence, an observer is qDES if its error dynamics are input-to-state stable (ISS) with respect to the disturbance as long as the plant's input and state remain bounded. We develop Lyapunov-based sufficient conditions for checking the qDES property for both full-order and reduced-order observers. This relates to a novel "asymptotic ratio" characterization of ISS which is of interest in its own right. When combined with a state feedback law robust to state estimation errors in the ISS sense, a qDES observer can be used to achieve output feedback control design with robustness to measurement disturbances. As an application of this idea, we treat a problem of stabilization by quantized output feedback. Applications to synchronization of electric power generators and of chaotic systems in the presence of measurement errors will also be discussed.
During the past decades model predictive control (MPC) has become a preferred control strategy for the control of a large number of industrial processes. Computational issues, application aspects and systems theoretic properties of MPC (like stability and robustness) are rather well understood by now. For many application disciplines a significant shift in the typical control tasks to be solved can, however, be witnessed at present. This concerns for example robot control, autonomous mobility, or industrial production processes. This will be examplarily discussed with the vision of the smart factory of the future, often termed Industry 4.0, where the involved control tasks, are undergoing a fundamental new orientation. In particular the stabilization of predetermined setpoints does not play the same role as it has in the past. In this talk we will first give an introduction to and an overview over the field of model predictive control. Then new challenges and opportunities for the field of control are discussed with Industry 4.0 as an example. We will in particular investigate the potential impact of Model Predictive Control for the fourth industrial revolution and will argue that some new developments in MPC, especially connected to distributed and economic model predictive control, appear to be ideally suited for addressing some of the new challenges.
Geometric mechanics is useful in developing a compact description of the motion of a rigid body in three-dimensional space which is singularity-free, unique, does not limit the motion to small angles, and enables a single control law to be obtained even in the presence of translational/rotational coupling. Such a description, which is based on the Lie group SE(3) and its corresponding "exponential coordinates", is especially useful for spacecraft and other types of autonomous vehicles undergoing fast rotations and tumbling motions. This talk will explore various coordinates for rigid body attitude along with their pros and cons (including the phenomenon of unwinding when using a quaternion attitude description) as well as the use of the SE(3) framework in multi-vehicle consensus control design in which it is desired to achieve leader-follower formations along with attitude synchronization. The case of four formation flying spacecraft in a Molniya orbit will serve as an illustrative example.
Feedback is as ubiquitous in nature as it is in design. So control theory can help us understand both natural and designed systems. Even better, generalized models abstracted from nature give us a mathematical means to connect control theoretic explanations of nature with opportunities in control design. Control theory is enriched by the language, questions, and perspectives of fields as diverse as animal behavior, cognitive science, and dance. I will present a model for multi-agent dynamics that is informed by these fields. The model derives from principles of symmetry and bifurcation, which exploit instability to recover the remarkable capacity of natural groups to trade off flexibility and stability.
In many application domains, including systems and control theory, the optimization problems that appear are seldom "generic" but instead they often have well-defined structural features. Depending on the situation, such structure may be described algebraically (e.g., by transformations under which the problem is invariant, like linearity or time-invariance), geometrically (by restricting the feasible set to a given manifold/variety), or graphically (e.g., by a graph summarizing the interactions among decision variables). Exploiting this structure is crucial for practical efficiency. In this talk we will provide a gentle introduction to these ideas, surveying the basic notions as well as describing algorithmic techniques to detect and exploit these properties. In particular, we will discuss some recent developments, including dimension/symmetry reduction techniques for SDPs, and chordal networks. As we will illustrate through applications, algorithms that automatically exploit structure can significantly outperform existing techniques.
The 25th anniversary of the commercialization of lithium-ion batteries marks their wide-spread use in handheld consumer electronics and coincides with a period of intense efforts for powering electric vehicles. Managing the potent brew of lithium ions in the large quantities necessary for vehicle propulsion is anything but straightforward. Designing the complex conductive structure, choosing the electrode material for locking the energy in high potential states and synthesizing the interfaces for releasing the chemical energy at fast but controllable rates has been the focus of the electrochemists and material scientists. But from the Rosetta-Philae spacecraft landing three billion miles away from Earth to the daily commute of a hybrid electric automobile, the control engineers behind the battery management system (BMS) have been the unsung heroes. The BMS is the brain of the battery system and is responsible for State of Charge (SOC), State of Health (SOH) and State of Power (SOP) estimation while protecting the cell by limiting its power. The BMS relies on accurate prediction of complex electrochemical, thermal and mechanical phenomena. This raises the question of model and parameter accuracy. Moreover, if the cells are aging, which parameters should we adapt after leveraging limited sensor information from the measured terminal voltage and sparse surface temperatures? With such a frugal sensor set, what is the optimal sensor placement? To this end, control techniques and novel sensors that measure the cell swelling during lithium intercalation and thermal expansion will be presented. We will conclude by highlighting the fundamental difficulties that keep every battery control engineer awake, namely predicting local hot spots, detecting internal shorts, and managing the overwhelming energy released during a thermal runaway.
Recent work on Model Predictive Control has refocused attention on the role of future disturbance uncertainty. One way of dealing with this issue is to use policy rather than sequence optimization. However, this comes at a significant increase in computational burden. In this talk we will outline strategies for dealing with the computational issue, including using quantized scenarios to represent the future disturbances. The related issue of providing performance guarantees in the face of high uncertainty will also be discussed. The ideas will be illustrated by the development of a new treatment strategy for Type 1 diabetes mellitus.
What is model reference adaptive control? Why does one prefer using a model reference adaptive controller? How can we design and analyze a model reference adaptive controller? In this FoRCE video, we answer these fundamental questions related to model reference adaptive control theory and beyond.
More information provided here.
The area of polynomial optimization has been actively studied in computer science, operations research, applied mathematics and engineering, where the goal is to find a high-quality solution using an efficient computational method. This area has attracted much attention in the control community since several long-standing control problems could be converted to polynomial optimization problems. The current researches on this area have been mostly focused on various important questions: i) how does the underlying structure of an optimization problem affect its complexity? Ii) how does sparsity help? iii) how to find a near globally optimal solution whenever it is hard to find a global minimum? iv) how to design an efficient numerical algorithm for large-scale non-convex optimization problems? v) how to deal with problems with a mix of continuous and discrete variables? In this talk, we will develop a unified mathematical framework to study the above problems. Our framework rests on recent advances in graph theory and optimization, including the notions of OS-vertex sequence and treewidth, matrix completion, semidefinite programming, and low-rank optimization. We will also apply our results to two areas of power systems and distributed control. In particular, we will discuss how our results could be used to address several hard problems for power systems such as optimal power flow (OPF), security-constrained OPF, state estimation, and unit commitment.
The number of unmanned aerial vehicles or drones has grown exponentially over the last three decades. Yet we are only now seeing autonomous flying robots that can operate in three-dimensional indoor environments and in outdoor environments without GPS. I will discuss the need for smaller, safer, smarter, and faster flying robots and the challenges in control, planning, and coordinating swarms of robots with applications to search and rescue, first response and precision farming. Publications and videos are available at kumarrobotics.org.
Network systems have received a lot of attention in the past decade. They are used to analyze and design communication network, smart grid technology, social media, social dynamics, formation and consensus problems, etc. Several analysis and control methods have been developed for network systems. However, often, their large scale nature makes it difficult to analyze and to design a controller. We develop methods to reduce the order of the network while preserving the network structure, as well as some structure of the (linear) node dynamics. In particular, second order network dynamics structure is preserved. We use node clustering methods, as well as a state space singular value decomposition based method. For the first we provide error bounds. We illustrate the results with help of some relevant high order examples.
Model predictive control has become a pervasive advanced control technology in which optimal control of a multivariable system with input and state constraints is combined with a moving horizon to produce a feedback controller. In applications, model predictive control is often used to solve constrained tracking problems. The tracking problem arises in some settings as the basic goal of the control system, and the constraint handling capabilities of MPC are what make it attractive. In other applications, however, there may be a higher-level goal, such as economic optimization of a process, and this goal is first translated into a steady-state tracking problem. Since MPC enables the designer to choose the objective function that is optimized online, it offers the potential to treat the higher-level control goal directly within the MPC controller bypassing this translation into a steady-state setpoint and tracking problem. In this talk we explore the possibilities enabled by MPC to address these types of high-level goals. We also outline some of the open research challenges presented by this approach; these include modeling, optimization, and controller design challenges. The talk concludes with a brief presentation of a recently deployed economic optimization technology developed by Johnson Controls to control the campus energy system at Stanford University.
The field of control provides the principles and methods used to design physical, biological and information systems that maintain desirable performance by sensing and automatically adapting to changes in the environment. The opportunities to apply control principles and methods are exploding. In this talk I will briefly review some of the past predictions for future directions in control (including some of my own) and provide some thoughts on how well the field is doing in terms of living up to its past promises of future success. The ultimate goal of the talk is to help inspire the next generation of controls researchers, balance theory with application, provide a view into the possible futures of control, give credit where it is due, and let the guard down and talk about personal stuff a bit.
Distributed and large-scale optimization problems have gained a significant attention in the context of cyber-physical, peer-to-peer, and ad-hoc networked systems. The large-scale property is reflected in the number of decision variables, the number of constraints, or both, while the distributed nature of the problems is inherent due to partial (local) knowledge of the problem data (e.g., a portion of the cost function or a subset of the constraints is known to different entities in the system). The talk will focus on some recent developments on optimization models and algorithmic approaches for solving such problems with applications in domains ranging from control to machine learning.
Smart Cities are an example of Cyber-Physical Systems whose goals include improvements in transportation, energy distribution, emergency response, and infrastructure maintenance, to name a few. One of the key elements of a Smart City is the ability to monitor and dynamically allocate its resources. The availability of large amounts of data, ubiquitous wireless connectivity, and the critical need for scalability open the door for new control and optimization methods which are both data-driven and event-driven. The talk will present such an optimization framework and its properties. It will then describe several applications that arise in Smart Cities, some of which have been tested in the City of Boston: a “Smart Parking” system which dynamically assigns and reserves an optimal parking space for a user (driver); the “Street Bump” system which uses standard smartphone capabilities to collect roadway obstacle data and identify and classify them for efficient maintenance and repair; adaptive traffic light control; optimal control of connected autonomous vehicles. Lastly, to address the “social’’ dimension, the talk will describe how a large traffic data set from the Massachusetts road network was analyzed to estimate the Price of Anarchy in comparing “selfish” user-centric behavior to “social” system-centric optimal traffic routing solutions.
