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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/.
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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.
Urban mobility in Transportation is witnessing a transformation due to the emergence of new concepts in Mobility on Demand, where new modes of transportation other than private individual cars and public mass transit are being investigated. With a projection of a total number of 2 billion vehicles on roads by the year 2050, such innovations in transportation are urgently needed. One such paradigm is the notion of shared mobility on demand, which consists of customized dynamic routing for multi-passenger transport. A solution to this problem consists of a host of challenges that ranges from distributed optimization, behavioral modeling of passengers, traffic flow modeling, and distributed control. Recent efforts in our group have made some inroads into this problem and form the focus of this talk. A socio-technical model that combines behavioral models of passengers based on Cumulative Prospect Theory and traffic models will be discussed. The solution to dynamic routing is presented in the form of an optimization problem solved via an Alternating Minimization based approach. The model together with the optimization framework is then used to propose a dynamic tariff that can be viewed as a model-based control strategy based on Transactive Control, a methodology that is being explored in power grids for incentivizing flexible consumption.
The well-functioning of our modern society rests on the reliable and uninterrupted operation of large scale complex infrastructures, which are more and more exhibiting a network structure with a high number of interacting components/agents. Energy and transportation systems, communication and social networks are a few, yet prominent, examples of such large scale multi-agent networked systems. Depending on the specific case, agents may act cooperatively to optimize the overall system performance or compete for shared resources. Based on the underlying communication architecture, and the presence or not of a central regulation authority, either decentralized or distributed decision making paradigms are adopted. In this seminar, we address the interacting and distributed nature of cooperative multi-agent systems arising in the energy application domain. More specifically, we present our recent results on the development of a unifying distributed optimization framework to cope with the main complexity features that are prominent in such systems, i.e.: heterogeneity, as we allow the agents to have different objectives and physical/technological constraints; privacy, as we do not require agents to disclose their local information; uncertainty, as we take into account uncertainty affecting the agents locally and/or globally; and combinatorial complexity, as we address the case of discrete decision variables. (This is a joint work with Alessandro Falsone, Simone Garatti, and Kostas Margellos.)
Automated and connected road vehicles enable large-scale control and optimization of the transport system with the potential to radically improve energy efficiency, decrease the environmental footprint, and enhance safety. Freight transportation accounts for a significant amount of all energy consumption and greenhouse gas emissions. In this talk, we will discuss the potential future of road goods transportation and how it can be made more robust and efficient, from the automation of individual long-haulage trucks to the optimization of fleet management and logistics. Such an integrated cyber-physical transportation system benefits from having trucks traveling together in vehicle platoons. From the reduced air drag, platooning trucks traveling close together can save more than 10% of their fuel consumption. In addition, by automating the driving, it is possible to change driver regulations and thereby increase the efficiency even more. Control and optimization problems on various level of this transportation system will be presented. It will be argued that a system architecture utilizing vehicle-to- vehicle and vehicle-to-infrastructure communication enable robust and safe control of individual trucks as well as optimized vehicle fleet collaborations and new market opportunities. Furthermore, feedback control of individual platoons utilizing the cellular communication infrastructure can be used to improve the overall traffic conditions by reducing the variation of traffic density. Extensive experiments done on European highways will illustrate system performance and safety requirements. The presentation will be based on joint work over the last ten years with collaborators at KTH and at the truck manufacturers Scania and Volvo.
Autonomous systems use closed-loop feedback of sensed or communicated information to meet desired objectives. Meeting such objectives is more challenging when autonomous systems are tasked with operating in uncertain complex environments with intermittent feedback. This presentation explores different analysis methods that quantify the effects of intermittent feedback with respect to stability and performance of the autonomous agent. Various scenarios are considered where the intermittency results from natural phenomena or adversarial actors, including purposeful intermittency to enable new capabilities. Specific examples include intermittency due to occlusions in image-based feedback and intermittency resulting from various network control problems.
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.
Cyber-physical systems are the basis for the new industrial revolution. Growing energy demand and environmental concerns require a large number of renewable energy resources, efficient energy consumption, and energy storage devices and demand responses. Cyber-physical energy systems (CPES) in a broader sense provide a desirable infrastructure for efficient energy production and consumption with uncertain energy resources. This speech will focus on the structure of CPES, and the problem of security-constrained planning and scheduling of CPES, including new renewable energy sources with high levels of uncertainties. The newly developed analytical conditions are discussed for fast identifying the security bottlenecks in a complex power grid when new renewable energy sources coordinate with storable energy sources such as hydro and pumped storages. A new method is introduced to solve the well-knows N-k contingency security assessment problem with 2-3 orders of reduced computational complexity. Production, storage and transportation, and utilization of hydrogen as the main energy source are also introduced. It is shown that the hydrogen-based CPES will provide an ideal infrastructure for energy supply and consumption with almost no pollution, and it will likely lead to the energy revolution anticipated in the new century.
In the six decades of conventional TUNING-BASED adaptive control, the unattained fundamental goals, in the absence of detrimental artificial excitation, have been (1) exponential regulation, as with robust controllers, and (2) perfect learning of the plant model. More than a quarter-century since I started my career by extending conventional adaptive controllers from linear to nonlinear systems, I reach those decades-old goals with a new non-tuning paradigm: regulation-triggered batch identification. The parameter estimate in the controller is held constant and, only once the regulation error grows “too large,” a parameter estimate update, based on the data since the last update, is “triggered.” Such a simple parameter estimator provably, and remarkably terminates updating after a number of state growth-triggered updates which is no greater than the number of unknown parameters. This yields exponential regulation and perfect identification except for zero-measure initial conditions. I present a design for a more general class of nonlinear systems than ever before, an extension to adaptive PDE control, a flight control example (the “wing rock” instability), and, time permitting, a simple robotics example. This is joint work with Iasson Karafyllis from the Mathematics Department of the National Technical University of Athens.
Probability theory has had a significant impact on systems and controls. In this talk, we will visit three developments in control theory that has a close connection to, and impacted by, the results in probability theory. We discuss Perron-Frobenius Theorem and its relation to the results in distributed computation and optimization, and its generalization to time-varying chains. We will discuss a result in controllability of random networks and show how the recent developments in random matrix theory and inverse Littlewood-Offord Theory shed light on such problems. Lastly, we discuss controllability of safety-critical stochastic systems and how Martingale theory leads to design and analysis of control policies for stochastic systems.
Recent years have seen a great progress in the area of robotics. Communication signals are also ubiquitous these days. In this talk, I will explore the opportunities and challenges at this intersection, for robotic sensing and communication. In the first part of the talk, I will focus on robotic sensing, and ask the following question "Can everyday communication signals, such as WiFi signals, give new sensing capabilities to unmanned vehicles?" For instance, imagine two unmanned vehicles arriving behind thick concrete walls. Can they image every square inch of the invisible area through the walls with only WiFi signals? I will show that this is indeed possible, and discuss how our methodology for the co-optimization of path planning and communication has enabled the first demonstration of 3D imaging through walls with only drones and WiFi. I will also discuss other new sensing capabilities that have emerged from our approach, such as occupancy estimation and crowd analytics with only WiFi signals. In the second part of the talk, I will focus on communication-aware robotics, a term coined to refer to robotic systems that explicitly take communication issues into account in their decision making. This is an emerging area of research that not only allows a team of unmanned vehicles to attain the desired connectivity during their operation, but can also extend the connectivity of the existing communication systems through the use of mobility. I will then discuss our latest theoretical and experimental results along this line. I will show how each robot can go beyond the over-simplified but commonly-used disk model for connectivity, and realistically model the impact of channel uncertainty for the purpose of path planning. I will then show how the unmanned vehicles can properly co-optimize their communication, sensing and navigation objectives under resource constraints. This co-optimized approach can result in a significant performance improvement and resource saving, as we shall see. I will also discuss the role of human collaboration in these networks.
The goal of reinforcement learning is at the core of the CSS mission: computation of policies that are approximately optimal, subject to information constraints. From the beginning, control foundations have lurked behind the RL curtain: Watkins’ Q-function looks suspiciously like the Hamiltonian in Pontryagin’s minimum principle, and (since Van Roy’s thesis) it has been known that our beloved adjoint operators are the key to understanding what is going on with TD-learning. This talk will briefly survey the goals and foundations of RL, and present new work showing how to dramatically accelerate convergence based on a combination of control techniques. The talk will include a wish-list of open problems in both deterministic and stochastic control settings.
The notion of what constitutes a robot has evolved considerably over the past five decades, from simple manipulator arms to large networks of interconnected autonomous and semi-autonomous agents. A constant in this evolutionary development has been the central nature of control theory in robotics to enable a vast array of applications in manufacturing automation, field and service robotics, medical robotics and other areas. In this talk we will present an historical perspective of control in robotics together with specific results in passivity-based control and control of underactuated robots. Finally, we will speculate about the future role of control theory in robotics in the era of human-robot interaction, machine learning, and big data analytics.
