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Snake robots are motivated by the slender and flexible body of biological snakes, which allows them to move in virtually any environment on land and in water. Since the snake robot is essentially a manipulator arm that can move by itself, it has a number of interesting applications including firefighting and search-and-rescue operations. In water, the robot is a highly flexible and dexterous manipulator arm that can swim by itself like a sea snake. This highly flexible snake-like mechanism has excellent accessibility properties, and not only can the snake robot access narrow openings and confined areas, it can also carry out highly complex manipulation tasks at this location since manipulation is an inherent capability of the system. This talk presents research results on modelling, analysis and control of snake robots, including both theoretical and experimental results. Ongoing efforts are described for bringing the results from university research towards industrial use.
Machine learning-based techniques have recently revolutionized nearly every aspect of autonomy. In particular, deep reinforcement learning (RL) has rapidly become a powerful alternative to classical model-based approaches to decision-making, planning, and control. Despite the well-publicized successes of deep RL, its adoption in complex and/or safety-critical tasks at scale and in real-world settings is hindered by several key issues, including high sample complexity in large-scale problems, limited transferability, and lack of robustness guarantees. This talk explores our recently developed solutions that address these fundamental challenges for both single and multiagent RL. In addition, this talk highlights the complementary role that classical model-based techniques can play in synergy with data-driven methods in overcoming these issues. Real experiments with ground and aerial robots will be used to illustrate the effectiveness of the proposed techniques. The talk will conclude with an assessment of the state of the art and highlight important avenues for future research.
There are many interesting dynamical systems that can be regarded as hierarchically networked systems in a variety of fields including control. One of the ideas to treat those systems properly is "Glocal (Global/Local) Control," which means that the global purpose is achieved by local actions of measurement and control cooperatively. The key for realization of glocal control is hierarchically networked dynamical systems with multiple resolutions in time and space depending on the layer, which introduce many new theoretical control challenges aiming at practical effectiveness in synthetic biology and engineering. The main issues may include how to achieve synchronization by decentralized control and how to make a compromise of two different objectives, one for global and the other for local operations. The background, the idea, and the concept of glocal control are addressed based on an understanding of Internet of Things (IoT) from the control perspective. This talk presents two research topics, namely, (1) hierarchically decentralized control for networked dynamical systems, and (2) robust instability analysis for a class of uncertain nonlinear networked systems.
Regarding the first topic, we propose a theoretical framework for hierarchically decentralized control of networked dynamical systems that can take account of the tradeoff between the global and local objectives to achieve the desired harmony under change of the environments. Several new ideas, by exploiting the special structure of the target systems, enable us to develop scalable control design methods based on the powerful theory in classical, modern, and robust control. The effectiveness of the new theoretical foundations on the analysis and synthesis is experimentally confirmed by applications to electric vehicle control.
The second topic is quite new. It is on robust instability analysis for guaranteed persistence of nonlinear oscillations in the presence of a dynamic perturbation, which is important in synthetic biology. The problem of robust instability has a very different feature from that of robust stability, and hence a new theoretical setting is needed. We define the instability margin as the infimum of the H-infinity norm of the stable perturbation that stabilizes an equilibrium point for a class of nonlinear networked systems. To this end, we introduce a notion of the robust instability radius (RIR) for linear systems and provide a systematic way of finding the exact RIR. Based on this result, the instability margin can be analyzed exactly, with an additional theoretical investigation on how to properly treat the change of the equilibrium point due to the perturbation. The results are applied to the Repressilator in synthetic biology, and the effectiveness is confirmed by numerical simulations.
Mathematics plays a fundamental role in disciplines such as physics, engineering, computer science, and chemistry and has been more recently accepted as a suitable language for solving problems in biology, biochemistry, and medicine.
Control theory is part of the mathematical world and has the peculiarity of borrowing tools from different branches of mathematics. Interestingly, many of the techniques conceived and routinely used to solve control problems can be quite successfully adapted to solve new relevant problems, both practical and curiosity-driven, in other fields.
This talk discusses the structural analysis of systems, aimed at explaining how mechanisms work, why they work in a certain way, and to which extent they perform their task properly even in the presence of perturbations and disturbances.
The first part of the talk briefly introduces some preliminary motivating examples of mechanisms, borrowed from other disciplines alien to control theory, to show how a control approach can be very powerful to understand fundamental principles.
The second part introduces the definitions of structural versus robust properties, discussing paradigmatic case studies from the literature. Robust stability analysis is presented in an inverse form: "We know that this system is stable, but why is the system so incredibly stable?". Other fundamental concepts such as (perfect) adaptation, structural steady-state analysis, graph loop analysis, and aggregation are considered.
The third part discusses application examples from biology and biochemistry, to showcase the potential impact that the mathematical approach of control theory, suitably revised, can have in these disciplines and how interdisciplinary research can bring fresh ideas to control theorists.
Existing control design and verification methods are limited in their ability to address large numbers of interacting agents, multiple layers of feedback, and complex system-level requirements. This talk will demonstrate a strategy for overcoming this limitation with compositional and hierarchical approaches. The compositional approach exposes a complex system as an interconnection of smaller subsystems and derives system-level guarantees from subsystem properties. The hierarchical approach decomposes the synthesis and verification tasks into layers, from high-level decision making to low-level control synthesis. Taken together, these approaches break apart intractably large design and verification problems into subproblems of manageable size. In addition to broadly applicable methodology, the talk will present numerous motivating applications and experimental results, involving multicellular biological systems, fleets of autonomous vehicles, and a multiscale traffic management system.
Control policies that involve the real-time solution of one or more convex optimization problems include model predictive (or receding horizon) control, approximate dynamic programming, and optimization-based actuator allocation systems. They have been widely used in applications with slower dynamics, such as chemical process control, supply chain systems, and quantitative trading, and are now starting to appear in systems with faster dynamics. In this talk I will describe a number of advances over the last decade or so that make such policies easier to design, tune, and deploy. We describe solution algorithms that are extremely robust, even in some cases division free, and code generation systems that transform a problem description expressed in a high-level domain-specific language into source code for a real-time solver suitable for control. The recent development of systems for automatically differentiating through a convex optimization problem can be used to efficiently tune or design control policies that include embedded convex optimization.
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Recent radical evolution in distributed sensing, computation, communication, and actuation has fostered the emergence of cyber-physical network systems. Examples cut across a broad spectrum of engineering and societal fields. Regardless of the specific application, one central goal is to shape the network collective behavior through the design of admissible local decision-making algorithms. This is nontrivial due to various challenges such as the local connectivity, imperfect communication, model and environment uncertainty, and the complex intertwined physics and human interactions. In this talk, I will present our recent progress in formally advancing the systematic design of distributed coordination in network systems. We investigate the fundamental performance limit placed by these various challenges, design fast, efficient, and scalable algorithms to achieve (or approximate) the performance limits, and test and implement the algorithms on real-world applications.
Electrification of mobility and transport is a global megatrend that has been underway for decades. The mobility sector encompasses cars, trucks, busses, and aircraft. These systems exhibit complex interactions of multiple modes of power flow. These modes can be thermal, fluid, electrical, or mechanical. A key challenge in working across various modes of power flow is the widely varying time scales of the subsystems which makes centralized control efforts challenging. This talk will present a particular distributed controller architecture for managing the flow of power based on on-line optimization. A hierarchical approach allows for systems operating on different time scales to be coordinated in a controllable manner. It also allows for different dynamic decision-making tools to be used at different levels of the hierarchy based on the needs of the physical systems under control. Additional advantages include the modularity and scalability inherent in the hierarchy. Additional modules can be added or removed without changing the basic approach.
In addition to the hierarchical control, a particularly useful graph-based approach will be introduced for the purpose of modeling the system interactions and performing early-stage design optimization. The graph approach, like the hierarchy, has the benefits of modularity and scalability along with being an efficient framework for representing systems of different time scales. The graph allows design optimization tools to be implemented and optimize the physical system design for the purpose of control. Recent results will be presented representing both generic interconnected complex systems as well as specific examples from the aerospace and automotive application domains.
To ensure safety, reliability, and productivity of industrial processes, artificial intelligence (AI) and machine learning techniques have been widely used in process industries for decades. The benefits of process monitoring and control are well documented and employed routinely in manufacturing. This talk will go over historical perspective and recent AI and machine learning successes in the areas of real-time analytics, deep learning, reinforcement learning, visualization, and feature engineering. Complex interaction between human decision and automated control will be discussed. Humans grow expertise by quickly adapting to abnormal conditions and using domain knowledge to generate creative solutions. However, reproducing human decisions across the enterprise is a challenge. A common misconception is that AI is to replace human decision. The talk will emphasize how AI and control systems must be complementary to make human decisions as efficient and consistent as possible. Human decision will remain a center piece of how to operate industrial processes in a safe, reliable, and productive manner.
Networked and robotic systems in emerging applications are required to operate safely, adaptively, and degrade gracefully while coordinating a large number of nodes. Distributed algorithms have consolidated as a means for robust coordination, overcoming the challenges imposed by the limited capabilities of each agent. However, plenty of problems still exist to break down the barriers of fast computation, make effective use of measured data, and understand large-scale limit effects. In this talk, I will present ongoing work in the control of infrastructure networks and large-swarm coordination, along with a discussion on modeling approaches, analysis tools, and architectural trade-offs going from small to large-sized robotic networks.
In September 2015, the Laser Interferometer Gravitational-wave Observatory (LIGO) initiated the era of gravitational wave astronomy (a new window on the universe) with the first direct detection of gravitational waves (ripples in the fabric of space-time) resulting from the merger of a pair of black holes into a single larger black hole. In August 2017 the LIGO and VIRGO collaborations announced the first direct detection of gravitational waves associated with a gamma ray burst and the electromagnetic emission (visible, infrared, radio) of the afterglow of a kilonova — the spectacular collision of two neutron stars. This marks the beginning of multi-messenger astronomy. The kilonova discovery was made using the U.S.-based LIGO; the Europe-based Virgo detector; and 70 ground- and space-based observatories.
The Advanced LIGO gravitational wave detectors are second generation instruments designed and built for the two LIGO observatories in Hanford, WA and Livingston, LA. These two identically designed instruments employ coupled optical cavities in a specialized version of a Michelson interferometer with 4 kilometer long arms. Resonant optical cavities are used in the arms to increase the interaction time with a gravitational wave, power recycling is used to increase the effective laser power and signal recycling is used to improve the frequency response. In the most sensitive frequency region around 100 Hz, the displacement sensitivity is 10^-19 meters rms, or about 10 thousand times smaller than a proton. In order to achieve this unsurpassed measurement sensitivity Advanced LIGO employs a wide range of cutting-edge, high performance technologies, including an ultra-high vacuum system; an extremely stable laser source; multiple stages of active vibration isolation; super-polished and ion milled optics, high performance multi-layer dielectric coatings; wavefront sensing; active thermal compensation; very low noise analog and digital electronics; complex, nonlinear multi-input, multi-output control systems; a custom, scalable and easily re-configurable data acquisition and state control system; and squeezed light. The principles of operation, the numerous control challenges and future directions in control will be discussed. More information is available at https://www.ligo.caltech.edu/.
We will illustrate the essential intuition behind the so-called "Model Recovery Anti-windup" scheme for handling input saturation in control systems design. The talk will mostly focus on the qualitative aspects of the core feature of the scheme: storage and recovery of the unconstrained response that would have occurred without saturation. This goal and the ensuing (model recovery) anti-windup solutions will be discussed and clarified by way of a number of simulated and experimental application studies, ranging from vibration isolation, open water channels, flight control systems, robotic arms, and brake-by-wire systems for motorcycles.
Model reference adaptive control is a powerful tool that has a capability to suppress the effect of system uncertainties for achieving a desired level of closed-loop system performance. Yet, for a wide array of applications including unmodeled dynamics such as coupled rigid body systems with flexible interconnection links, airplanes with high aspect ratio wings, and high speed vehicles with strong rigid body and flexible dynamics coupling, the closed-loop system stability with model reference adaptive control laws can be challenged. In this seminar, we will focus on the stability interplay between a class of unmodeled dynamics and system uncertainties for model reference adaptive control laws, and proposed a robustifying term to relax the resulting interplay. The presented system-theoretical findings will be also supported by experimental results in order to bridge the theory-practice gap, where we use a benchmark mechanical system setup involving an inverted pendulum on a cart coupled with another cart through a spring in the presence of unknown frictions.
Reachability analysis is the problem of evaluating the set of all states that can be reached by a system starting from a given set of initial states. Since the reachable set can rarely be computed exactly, a standard approach is to over-approximate this set as tightly as possible. Various set representations and methods have been proposed for finding over-approximations; however, they are computationally expensive and do not scale well to high dimensional systems. This is a particularly important shortcoming for “symbolic control,” where the designer must first generate a finite state transition system from a continuous state model with repeated reachability computations. In this talk we present a suite of methods that offer computational efficiency using a simpler set representation in the form of multi-dimensional intervals. These methods leverage nonlinear systems concepts, such as monotonicity and its variants, sensitivity of trajectories to initial conditions and parameters, and contraction properties. We further introduce data-driven approaches for problems where probabilistic guarantees are appropriate. As we demonstrate with examples interval representation and the associated methods are particularly well suited to symbolic control, but of independent interest as well.
Optimal controllers for linear or nonlinear dynamic systems with known dynamics can be designed by using Riccati and Hamilton-Jacobi-Bellman (HJB) equation respectively. However, optimal control of uncertain linear or nonlinear dynamic systems is a major challenge. Moreover, controllers designed in discrete-time have the important advantage that they can be directly implemented in digital form using modern-day embedded hardware. Unfortunately, discrete-time design using Lyapunov stability analysis is far more complex than the continuous-time counterpart since the first difference in Lyapunov function is quadratic in the states and not linear as in the case of continuous-time. By incorporating learning features with the feedback controller design, optimal adaptive control of such uncertain dynamical systems in discrete-time can be solved.
In this talk, an overview of first and second-generation feedback controllers with a learning component in discrete-time will be discussed. Subsequently, the discrete-time learning-based optimal adaptive control of uncertain nonlinear dynamic systems will be presented in a systematic manner using a forward in time approach based on reinforcement learning (RL)/approximate dynamic programming (ADP). Challenges in developing and implementing the three generations of learning controllers will be addressed using practical examples such as automotive engine emission control, robotics, and others. We will argue that discrete-time controller development is preferred for transitioning the developed theory to practice. Today, the application of learning controllers can be found in areas as diverse as process control, energy or smart grids, civil infrastructure, healthcare, manufacturing, automotive, transportation, entertainment, and consumer appliances. The talk will conclude with a short discussion of open research problems in the area of learning control.
Security and privacy are of growing concern in many control applications. Cyber attacks are frequently reported for a variety of industrial and infrastructure systems. For more than a decade the control community has developed techniques for how to design control systems resilient to cyber-physical attacks. In this talk, we will review some of these results. In particular, as cyber and physical components of networked control systems are tightly interconnected, it is argued that traditional IT security focusing only on the cyber part does not provide appropriate solutions. Modeling the objectives and resources of the adversary together with the plant and control dynamics is shown to be essential. The consequences of common attack scenarios, such as denial-of-service, replay, and bias injection attacks, can be analyzed using the framework presented. It is also shown how to strengthen the control loops by deriving security- and privacy-aware estimation and control schemes. Applications in building automation, power networks, and automotive systems will be used to motivate and illustrate the results. The presentation is based on joint work with several students and colleagues at KTH and elsewhere.
Advances in computing and networking technologies have connected manufacturing systems from the lowest levels of sensors and actuators, across the factory, through the supply chain, and beyond. Large amounts of data have always been available to these systems, with currents and velocities sampled at regular intervals and used to make control decisions, and throughputs tracked hourly or daily. The ability to collect and save this detailed low-level data, send it to a central repository, and store it for days, months, and years, enables better insight into the behavior – and misbehavior – of complex manufacturing systems. The output from high-fidelity models and/or reams of historical data can be compared with streams of data coming off the plant floor to identify anomalies. Early identification of anomalies, before they lead to poor quality products or machine failure, can result in significant productivity improvements. We will discuss multiple approaches for harnessing this data, leveraging both physics-based and data-driven models, and how automation can enable timely responses. Both simulation and experimental results will be presented.
