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I will give a brief tutorial of synthesis in a distributed setting, where the goal is to automatically synthesize, if they exist, a set of distributed observers or controllers which together achieve a specific goal. Rather than giving an exhaustive survey I will focus on specific aspects of the problem. In particular, my talk will be structured in two parts. In the first part I discuss theoretical aspects, specifically decidability and undecidability. In the second part I discuss distributed synthesis in practice, specifically, automatically synthesizing protocols such as the Alternating Bit Protocol using a combination of example scenarios and formal specifications as user inputs.
In this talk, we will give an overview of finite abstractions, which are graph-based representations for continuous-state control systems. If these finite abstractions are constructed properly, they can be used to design controllers using techniques from discrete event systems or reactive synthesis in a way that the designed controller can be implemented on the underlying continuous control system (namely, the concrete system) and provide guarantees on the closed-loop behavior. In order to lead to a correct-by-construction design, the abstract system should satisfy a certain relation with the concrete system. We will introduce several such relations including, (bi)simulation relations, over-approximations, feedback refinement relations, and discuss what type of properties are preserved under these relations. Finally, we will discuss various ways of constructing these abstractions, e.g., based on gridding or partitioning the state space, for different classes of systems, e.g., discrete-time or continuous-time. Several examples will be used throughout to demonstrate these techniques in action. The talk will conclude with a summary of more recent results and a discussion on several research directions.
We discuss recursive algorithms for state estimation and event inference, both of which are key tasks for monitoring and control of discrete event systems. In particular, we discuss algorithms for current-, initial-, and delayed-state estimation. We also discuss implications to various pertinent properties of interest, such as detectability (i.e., the ability to determine the exact system state after a finite number of events), diagnosability (i.e., the ability to detect within finite time the occurrence/type of a fault), and opacity (i.e., the guarantee that outsiders will never be able to infer that the system state lies within a set of certain secret/critical states). The talk also briefly discusses the extension of state estimation and event inference methodologies in emerging decentralized/distributed observation settings.
Swarm robotics, a subfield of both robotics and artificial swarm intelligence, focuses on the development of teams composed of large numbers of autonomous robotic agents. Like swarm intelligence, swarm robotics arises from the study of the phenomenology of biological systems in which large numbers of individuals collaborate in joint collective actions for the benefit of the community as a whole. However, whereas swarm intelligence often utilizes the means and mechanisms of bio-inspired swarms for numerical optimization, the goals of bio-inspired robot swarms are generally concerned with the use of large numbers of low-cost physically embodied agents, acting together in a real-world environment, to achieve a common purpose. This talk will discuss key methods and bio-inspired algorithms for use in programming and controlling robotic swarms, and potential applications of these swarms.
Opacity is an information-flow property used in privacy and security applications. A dynamic system is opaque if an external observer that knows the system model and makes online observations of its behavior is not able to detect with certainty some "secret" information about the system. We discuss various notions of opacity and their verification in the context of discrete event systems modeled by automata or transition systems: current-state opacity, initial-state opacity, and K-step opacity. Then we consider how to enforce opacity for systems that are not opaque. We focus on opacity enforcement using obfuscation, when an external interface edits the outputs of the system in order to confuse the observer. We present solution methodologies for different variations of this problem. We conclude with illustrative examples of opacity in the context of location privacy in location-based services.
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.
By now, most of us know that unconscious biases affect the workplace. These hidden, reflexive preferences shape our world views and can profoundly affect how welcoming and open a workplace is to different people and ideas. These predispositions shape the decisions we make by affecting the way we interpret information and how we interact with others—significantly impacting a whole host of organizational processes from recruitment to retention.
At the same time, we are experiencing significant shifts in global demographic trends that impact age, race, ethnicity, gender, religion, and LGBTQ employees. There is no doubt that our workplace is becoming more diverse, which increases the potential for more biases.
Customized bias scenarios (based on your audience) and real-world cases will be discussed. Several individual and organizational strategies to minimize bias will be provided.
During this interactive presentation, you will learn how to:
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/.
