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Integrated systems are ubiquitous as more heterogeneous physical entities are combined to form functional platforms. New and “invisible” feedback loops and couplings are introduced with increased connectivity, leading to emerging dynamics and making the integrated systems more control-intensive. The multi-physics, multi-time scale, and distributed-actuation natures of integrated systems present new challenges for modeling and control. Understanding their operating environments, achieving sustained high performance, and incorporating rich but incomplete data also motivate the development of novel design tools and frameworks.
In this talk, I will use the integrated thermal and power management of connected and automated vehicles (CAVs) as an example to illustrate the challenges in the prediction, estimation, and control of integrated systems in the era of rapid advances in AI and data-driven control. While first-principle-based modeling is still essential in understanding and exploiting the underlying physics of the integrated systems, model-based control and optimization have to be used in a much richer context to deal with the emerging dynamics and inevitable uncertainties. For CAVs, we will show how model-based design, complemented by data-driven approaches, can lead to control and optimization solutions with a significant impact on energy efficiency and operational reliability, in addition to safety and accessibility.
I have thoroughly enjoyed teaching and research in the field of mechanical systems control over the past fifty years. This field has been full of new theory, new mechanical hardware and new tools for real time control, and is nothing but the world of mechatronics. In this talk, I would like to give a brief review of how this field has developed during the past fifty years and what my personal involvements have been in this field and what my current involvements are. Overall, the talk is a chronicle of my journey of exploration with my students in the forest of mechanical systems control.
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Ensemble control deals with the problem of using a common control input to simultaneously steer a large population (in the limit, a continuum) of individual control systems. In this talk, we address a fundamental problem in ensemble control theory, namely, system controllability. A key factor in determining controllability of an ensemble system is its underlying parameterization space. Roughly speaking, the bigger the parameterization space is, the more difficult one can control the ensemble. Over the past two decades, significant progress has been made for understanding controllability of ensemble systems over one-dimensional parameterization spaces, yet little is known when the dimensions are greater than one. A major focus of this talk is to present recent advances in controllability of ensemble systems whose parameterization spaces are multi-dimensional. We will consider two classes of ensemble systems, namely, ensembles of linear control systems and ensembles of control-affine systems. We will first show that linear ensemble systems are problematic if their parameterization spaces are greater than one and, then, show how to resolve this controllability issue by using a special class of control-affine ensembles whose control vector fields are equipped with a fine structure.
Control systems with learning abilities could cost-effectively address societal issues like energy reliability, decarbonization, climate security and enable autonomous scientific discovery. Recent investigations focus on longstanding challenges such as robustness, uncertainty, and safety of complex engineered systems. But most importantly, innovation in deep learning methods, tools, and technology offers an unprecedented opportunity to transform the control engineering practice and bring much excitement to control systems theory research. In this talk, I will introduce recent results in modeling dynamic systems with deep learning representations that embed domain knowledge. I will also discuss differentiable predictive control, a data-driven approach that uses physics-informed deep learning representations to synthesize predictive control policies. I’ll illustrate the concepts with examples from various engineering applications. I’ll close by considering the implications of differentiable programming on the broader control systems context.
Evolution over the course of 500 million years has endowed fish with superior swimming and sensory capabilities in water. This has not only captivated the interest of biologists, but also spurred the development of underwater machines aiming to emulate fish’s locomotion and sensing marvels. In this talk I will first discuss efforts in developing hydrodynamic sensing systems inspired by lateral lines, the flow-sensing organ of fish. I will then illustrate the important role played by advanced modeling and control tools in optimizing robotic fish’s locomotion performance. I will further introduce gliding robotic fish, a new class of robotic fish that incorporates gliding to boost locomotion energy-efficiency, and discuss its application to autonomous underwater sensing. In one example, the unique spiral dynamics of gliding robotic fish is exploited in sampling the distribution of harmful algae along water columns. In another example, a network of gliding robotic fish is proposed for tracking the movement of invasive fish species with acoustic telemetry, and we show how distributed filtering algorithms can be used to localize the moving target. Both examples will be supported with results from field experiments.
The future of healthcare will involve personalized medical therapies for individuals. In applications involving the delivery of a drug (for example, insulin), such personalization can be achieved through the use of tailored feedback control strategies. For close to 30 years, our research group has collaborated with medical experts on the design of algorithms for safe and effective insulin delivery for individuals with Type 1 diabetes mellitus (T1DM). T1DM is a chronic autoimmune disease affecting approximately 35 million individuals world-wide, with associated annual healthcare costs in the US estimated to be approximately $15 billion. Over the years, there have been remarkable innovations in glucose measurement technology, insulin pump design, and personalized control algorithms. Over the last 5 years, multiple commercial closed-loop devices have entered the market, thus delivering the so-called “artificial pancreas” to individuals with T1DM. In this talk, I will outline the difficulties inherent in controlling physiological variables, the challenges with regulatory approval of such devices, and will describe several control systems engineering algorithms we have tested in clinical and outpatient settings for the artificial pancreas. I will describe our work in creating an embedded version of our MPC algorithm to enable a portable implementation in a medical IoT framework and will highlight some of the open challenges for automated insulin delivery. I’ll close by sharing other medical examples where feedback algorithms could provide transformational advances in personalized medicine, including chronotherapy.
People tend to overtrust sophisticated computing devices, especially those powered by AI. As these systems become more fully interactive with humans during the performance of day-to-day activities, ethical considerations in deploying these systems must be more carefully investigated. Bias, for example, has often been encoded in and can manifest itself through AI algorithms, which humans then take guidance from, resulting in the phenomenon of excessive trust. Bias further impacts this potential risk for trust, or overtrust, in that these cyber-physical systems are learning by mimicking our own thinking processes, inheriting our own implicit gender and racial biases, for example. These types of human-AI feedback loops may consequently have a direct impact on the overall quality of the interaction between humans and machines, whether the interaction is in the domains of healthcare, job-placement, or other high-impact life scenarios. In this talk, we will discuss various forms of bias, as embedded in our machines, and possible ways to mitigate its impact on cyber-physical human systems.
In this talk, we will present some of our recent results and ongoing work on safety-critical control synthesis under state and time (spatiotemporal) constraints and input constraints, with some applications in multi-robot systems. The proposed framework aims to eventually develop and integrate estimation, learning and control methods towards provably-correct and computationally-efficient mission synthesis for multi-agent systems in the presence of spatiotemporal constraints and uncertainty.
Time-critical applications are often performed over a time interval [0, τ), where the utilized finite-time control algorithms are expected to assure a task completion at a user-defined convergence time τ. In this talk, we will explore how to address these applications using the time transformation approach, which allows us to transform a resulting algorithm over the prescribed time interval [0, τ) to an equivalent algorithm over the stretched infinite-time interval [0,∞) for stability analysis. In addition, a procedure for designing such finite-time control algorithms is presented. We further demonstrate the approach’s efficacy with numerical examples and experimental results involving networked multiagent systems.
There are two main approaches to control gain synthesis an internal model-based distributed dynamic state feedback control law for the linear cooperative output regulation problem: (i) agent-wise local design methods, (ii) global design methods. Agent-wise local design methods to synthesize distributed control gains focus on the individual dynamics of each agent to guarantee the overall stability of the system. They are powerful tools due to their scalability. However, the agent-wise local design methods are incapable of maximizing the overall system performance through, for example, decay rate assignment. On the other hand, design methods, which are predicated on a global condition, lead to nonconvex optimization problems. We present a convex formulation of this global design problem based on a structured Lyapunov inequality. Then, the existence of solutions to the structured Lyapunov inequality is investigated. Specifically, we analytically show that the solutions exist for the systems satisfying the agent-wise local sufficient condition. Finally, we compare the proposed method with the agent-wise local design method through numerical examples in terms of conservatism, performance maximization, graph dependency, and scalability.
Systems with dynamics evolving in distinct slow and fast timescales include aircraft (Khalil & Chen, 1990), robotic manipulators, (Tavasoli, Eghtesad, & Jafarian, 2009), electrical power systems (Sauer, 2011), chemical reactions (Mélykúti, Hespanha, & Khammash, 2014), production planning in manufacturing (Soner, 1993), and so on. The Geometric Singular Perturbation theory (Fenichel, 1979) is a powerful control law development tool for multiple-timescale systems because it provides physical insight into the evolution of the states in more than one timescale. The behaviour of the full-order system can be approximated by the slow subsystem, provided that the fast states can be stabilised on an equilibrium manifold. The fast subsystem describes how the fast states evolve from their initial conditions to their equilibrium trajectory or the manifold. This presentation develops two nonlinear, multiple-time-scale, output feedback tracking controllers for a class of nonlinear, nonstandard systems with slow and fast states, slow and fast actuators, and model uncertainties. The class of systems is motivated by aircraft with uncertain inertias, control derivatives, engine time-constant, and without direct measurement of angle-of-attack and sideslip angle. One controller achieves the control objective of slow state tracking, while the other does simultaneous slow and fast state tracking. Each controller is synthesized using time-scale separation, lower-order reduced subsystems, and estimates of unknown parameters and unmeasured states. The estimates are updated dynamically, using an online parameter estimator and a nonlinear observer. The update laws are so chosen that errors remain ultimately bounded for the full-order system. The controllers are simulated on a six-degree-of-freedom, high-performance aircraft model commanded to perform a demanding, combined longitudinal and lateral/directional maneuver. Even though two important aerodynamic angles are not measured, tracking is adequate and as good as a previously developed full-state feedback controller handling similar parametric uncertainties. Additionally, even though the two controllers in theory achieve two different control objectives, it is possible to choose either one of them for the same maneuver. Of the two new output feedback controllers, the slow state tracker accomplishes the maneuver with less control effort, while the simultaneous slow and fast state tracker does so with a smaller number of gains to tune.
