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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.
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.
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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.
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.
This talk examines the transient modeling of power flow for transient thermal systems. The focus is on dynamic phenomena starting with a basic thermodynamic cycle and building up to more complex systems. The overall goal of the modeling process is to develop systems-level models that are sufficiently flexible to be used on a variety of different applications. These models balance complexity with accuracy to obtain models that are sufficient for dynamic optimization and design as well as control algorithms In addition to the modeling approach we present control strategies aimed at managing the flow of thermal power. We present a particular hierarchical approach to power flow that accommodates multiple power modes. The hierarchy allows for systems operating on different time scales to be coordinated. It also allows for different control tools to be used at different levels of the hierarchy based on the needs of the physical systems under control. Stability results exploit the system structure to provide guarantees. Recent results will be presented representing both interconnected complex systems with specific examples from industrial partners.
At the quantum level, feedback loops have to take into account measurement back-action. The goal of this talk is to explain, in a tutorial way and on the first experimental realization of a quantum-state feedback, how such purely quantum effect can be exploited in models and stabilization control schemes. This closed-loop experiment was conducted in 2011 by the group of Serge Haroche (Physics Nobel Prize 2012). The control goal was to stabilize a small number of micro-wave photons trapped between two super-conducting mirrors and subject to quantum non-demolition measurement via probe off-resonant Rydberg atoms. The implemented control scheme was decomposed into two parts. The first part estimates in real-time the quantum state of the trapped photons via a discrete-time Belavkin quantum filter. The second part is a nonlinear quantum-state feedback based on control Lyapunov functions. It stabilizes via suitable coherent displacements the number of photon(s) towards its set-point, namely an integer less than 5 in the experiment. This control scheme relies on a hidden control Markov model whose structure combines three quantum rules: unitary deterministic Schrödinger evolution; stochastic collapse of the wave packet induced by the measurement; tensor product for the composite systems. These basic quantum rules characterize the structure of all Markovian models describing open-quantum systems. These rules explain also the existence to two kinds of feedback schemes currently developed for quantum error correction: measurement-based feedback where an open quantum system is stabilized by a classical controller; coherent or autonomous feedback (reservoir engineering) where an open quantum system is passively stabilized through its coupling with another highly dissipative quantum system, namely the quantum controller.
Thirty years ago, computer-aided control system design involved an exclusive community of engineers, typically in top research labs or large companies, running esoteric codes on timeshared minicomputers to design and analyze control algorithms, often for expensive systems produced in low volumes. Today, computer-aided control system design has grown into Model-Based Design, encompassing not only system analysis and algorithm design, but also implementation through code generation, plus verification and validation on both models and embedded code. It is used in every industry that creates today’s smart systems – aerospace, automotive, industrial automation, medical devices, robotics, energy, and many more – not only for the controls but integrating computer vision, communication, and machine learning. In this talk, Jack Little reviews the evolution of control design tools, and the corresponding changes in controls education and research. Jack then looks forward to the future of Model-Based Design and how it is addressing the next generation of control engineers: researchers and developers working on challenges such as cyber-physical systems and distributed systems, but also students and makers taking advantage of easy-to-use software with low-cost hardware – everyone building the smarter controlled systems of the future.
Many current products and systems employ sophisticated mathematical algorithms to automatically make complex decisions, or take action, in real-time. Examples include recommendation engines, search engines, spam filters, on-line advertising systems, fraud detection systems, automated trading engines, revenue management systems, supply chain systems, electricity generator scheduling, flight management systems, and advanced engine controls.
I'll cover the basic ideas behind these and other applications, emphasizing the central role of mathematical optimization and the associated areas of machine learning and automatic control. The talk will be nontechnical, but the focus will be on understanding the central issues that come up across many applications, such as the development or learning of mathematical models, the role of uncertainty, the idea of feedback or recourse, and computational complexity.
In this talk, I will describe some of our work on nanomechanics of biological systems and design of medical devices for hospitals in resource poor countries. These may sound like very disparate areas. However, you may be surprised to see how well the skills students learn in one translate well to the other. Atomic Force Microscopy and high precision instrumentation are common tools for the basic sciences. We can use these systems to measure small-scale intermolecular forces and characterize the nano-structures of individual cellular components. These types of measurements help to build more accurate models of tissues and organs to predict behavior during disease and injury. Beyond the basic sciences, the same types of concepts and skills needed for nanoscience work can be applied to solve real-world engineering problems in resource poor hospitals today. Working with engineers and clinicians in Tanzania, our students have designed several novel solutions to problems they have seen in clinics. These range from infant warmers to ink-jet printed diabetes test supplies to basket woven neck braces. In addition, while in the hospitals, our students put their debugging skills to the test by helping to repair and maintain clinical devices and equipment. Experiences in the lab and in the field give students a rounded perspective on engineering and a clearer outlook on their future career paths.
Humans have the ability to walk with deceptive ease, navigating everything from daily environments to uneven and uncertain terrain with efficiency and robustness. With the goal of achieving human-like abilities on robotic systems, this talk presents the process of formally achieving bipedal robotic walking through controller synthesis inspired by human locomotion, and it demonstrates these methods through experimental realization on numerous bipedal robots and robotic assistive devices. Motivated by the hierarchical control present in humans, human-inspired virtual constraints are utilized to synthesize a novel type of control Lyapunov function (CLF); when coupled with hybrid system models of locomotion, this class of CLFs yields provably stable robotic walking. Going beyond explicit feedback control strategies, these CLFs can be used to formulate an optimization-based control methodology that dynamically accounts for torque and contact constraints while being implementable in real-time. This sets the stage for the unification of control objectives with safety-critical constraints through the use of a new class of control barrier functions provably enforcing these constraints. The end result is the generation of bipedal robotic walking that is remarkably human-like and is experimentally realizable, together with a novel control framework for highly dynamic behaviors on bipedal robots. Furthermore, these methods form the basis for achieving a variety of advanced walking behaviors—including multi-domain locomotion, e.g., human-like heel-toe behaviors—and therefore have application to the control of robotic assistive devices, as evidenced by the demonstration of the resulting controllers on multiple robotic walking platforms, humanoid robots and prostheses.
Robotic technology can: (i) deliver therapy to aid recovery after neurological disease; (ii) replace limb function following amputation; and (iii) provide assistance to restore function. This exciting new frontier of robotic applications requires sensitive but forceful physical interaction with a human, yet physical contact can severely de-stabilize robots. Despite these challenges, clinical evidence shows that robot therapy is both effective and cost-effective. Motorized amputation prostheses present even greater challenges. They must manage physical interaction with objects in the world as well as with the amputee. This presentation will review how machine mimicry of natural control provides the gentleness required for robotic therapy and enables seamless coordination of natural and prosthetic limbs. A pre-requisite for success in these applications is a quantitative knowledge of the human motor control system.
Electricity production in the US has changed dramatically since 2000, with the percent of electricity produced from gas growing from 16% to 30%, while coal dropped from 52% to 37%. These changes are primarily driven by two technologies used in shale rock formations, directional drilling to create horizontal wells, and hydraulic fracturing to release the gas within the relatively impermeable rock. This presentation will first give a brief operational overview of hydraulic fracturing. Next, challenges that relate to the control of this technology are described. Lastly, two examples are presented, one a theoretical study investigating the potential of model-based feedback control of the hydraulic fracturing process and the other an implementation that highlights the importance of measurements and data uncertainty when designing effective and robust controllers.
