IEEE.org | IEEE Xplore Digital Library | IEEE Standards | IEEE Spectrum | More Sites
Call for Award Nominations
More Info
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.
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.
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.
Computer and communication technologies are rapidly developing leading to an increasingly networked and wireless world. This raises new challenging questions in the context of networked control systems, especially when the computation, communication and energy resources for control are limited. To efficiently use the available resources it is desirable to limit the control actions to instances when the system really needs attention. Unfortunately, classical time-triggered control schemes are based on performing sensing and actuation actions periodically in time (irrespective of the state of the system) rather than when the system actually needs attention. This points towards the consideration of event-triggered control as an alternative and (more) resource-aware control paradigm, as it seems natural to trigger control actions by well-designed events involving the system's state, output or any other locally available information: "To act or not to act, that is the question in event-triggered control." The objectives of this talk are to introduce the basics in the field of resource-aware control for distributed and multi-agent systems and to discuss recent advances and open questions. The focus will be on event-triggered control, although we will also touch upon self-triggered control as an alternative paradigm for resource-aware feedback control. We will show that various forms of hybrid systems, combining continuous and discrete dynamics, play instrumental roles in the analysis and the design of event-triggered and self-triggered controllers. The main developments will be illustrated in the context of cooperative driving exploiting wireless communication. The effects of delays, packet losses and (denial-of-service) attacks on the event-triggered cooperative adaptive cruise control (CACC) strategies for vehicle platooning will be discussed and experimental results will be presented.
Feedback is a key element of regulation, as it shapes the sensitivity of a process to its environment. Positive feedback up-regulates, negative feedback down-regulates. Many regulatory processes involve a mixture of both, whether in nature or in engineering. This paper revisits the mixed feedback paradigm, with the aim of investigating control across scales. We propose that mixed feedback regulates excitability and that excitability plays a central role in multi-scale signalling. We analyse this role in a multi-scale network architecture inspired from neurophysiology. The nodal behavior defines a meso-scale that connects actuation at the micro-scale to measurements at the macro-scale. We show that mixed-feedback control at the nodal scale provides regulatory principles at the network scale, with a nodal resolution. In this sense, the mixed feedback paradigm is a control principle across scales.
In cooperative multi-robot systems, there is a group of robots that seek to achieve a collective task as a team. Each individual robot makes decisions based on available local information as well as limited communications with neighboring robots. The challenge is to design local protocols that result in desired global outcomes. In contrast to a traditional centralized control paradigm, both measurements and decisions are distributed among multiple actors. This talk surveys various results for cooperative robotics based on methods drawn from game theory and distributed optimization, with applications to area coverage, cooperative pursuit, and self-assembly.
This seminar presents a survey of some of the main results in the theory of negative imaginary systems. The seminar also presents some applications of negative imaginary systems theory in the design of robust controllers. In particular, the seminar concentrates on the application of negative imaginary systems theory in the area of control of atomic force microscopes.
This talk will present models for the evolution of opinions, interpersonal influences, and social power in a group of individuals. I will present empirical data and mathematical models for the opinion formation process in deliberative groups, including concepts of self-weight and social power. I will then focus on groups who discuss and form opinions along sequences of judgmental, intellective, and resource allocation issues. I will show how the natural dynamical evolution of interpersonal influence structures is shaped by the psychological phenomenon of reflected appraisal. Multi-agent models and analysis results are grounded in influence networks from mathematical sociology, replicator dynamics from evolutionary games, and transactive memory systems from organization science. (Joint work with: Noah E. Friedkin, Peng Jia, and Ge Chen)
The interactions of dynamical systems communicating over a networked environment lead to intriguing synchronization behaviors with applications in Internet of Things, formations, satellite control, and human societal behaviors. This talk studies the relation between local controls design and communication graph restrictions. The distinctions between stability and optimality on graphs are explored. An optimal design method for local feedback controllers is given that decouples the control design from the graph structural properties. In the case of continuous-time systems, the optimal design method guarantees synchronization on any graph with suitable connectedness properties. In the case of discrete-time systems, a condition for synchronization is that the Mahler measure of unstable eigenvalues of the local systems be restricted by the condition number of the graph. Thus, graphs with better topologies can tolerate a higher degree of inherent instability in the individual node dynamics. A theory of duality between controllers and observers on communication graphs is given, including methods for cooperative output feedback control based on cooperative regulator designs. In second part of the talk, we discuss graphical games. Standard differential multi-agent game theory has a centralized dynamics affected by the control policies of multiple agent players. We give a new formulation for games on communication graphs. Standard definitions of Nash equilibrium are not useful for graphical games since, though in Nash equilibrium, all agents may not achieve synchronization. A strengthened definition of Interactive Nash equilibrium is given that guarantees that all agents are participants in the same game, and that all agents achieve synchronization while optimizing their own value functions.
This tutorial will describe the design of stable observers for nonlinear systems. The design methodology utilizes tools that include Lyapunov analysis, the Circle Criterion and the S-procedure Lemma. The observer stability conditions are typically obtained as linear or bilinear matrix inequalities from which the observer gains can be computed. The tutorial will start with a dynamic system in which the process dynamics has Lipschitz nonlinearities. This will later be generalized to allow for either Lipschitz, bounded Jacobian or sector bounded nonlinearities in both the process dynamics and the measurement equations. Simple programs to solve LMIs in Matlab and obtain the observer gains will also be presented. The lecture will conclude with the application of the developed methodology to automotive slip angle estimation in the presence of nonlinear tire force models.
As the characteristic size of a flying robot decreases, the challenges for successful flight revert to basic questions of fabrication, actuation, fluid mechanics, stabilization, and power — whereas such questions have in general been answered for larger aircraft. When developing a robot on the scale of a housefly, all hardware must be developed from scratch as there is nothing “off-the-shelf” which can be used for mechanisms, sensors, or computation that would satisfy the extreme mass and power limitations. With these challenges in mind, this talk will present progress in the essential technologies for insect-scale robots and the latest flight experiments with robotic insects.
The term capacity has natural connotations about fundamental limits and robustness to disruptions. For engineered systems, a rigorous characterization of capacity also provides insight into algorithms with universal performance guarantees and informs optimal strategic resource allocation. We present analysis and optimization of capacity and related performance metrics for societal cyber-physical systems (including traffic, mobility, and power networks) in canonical settings. At the macroscopic scale, we extend static network flow formulations to several flow dynamics and control settings (including cascading failure). The tractability of the resulting nonlinear analysis and optimization is facilitated by the spatial sparsity of dynamics and invariance of key input-output properties, such as monotonicity, across multiple resolutions in the network. At the microscopic scale, we consider spatial queues with state-dependent service rate; for example, such problems arise in networks of dynamically coupled vehicles. While this dependence is complex in general, we provide tight characterization in limiting cases, for instance large queue length, which leads to tight throughput estimates. Case studies are provided to evaluate the effectiveness of the proposed frameworks.
With its thin atmosphere, uncertain wind and terrain, and ever-increasing science requirements, robotic missions to the surface of Mars have presented enormous challenges to the Entry, Descent, and Landing (EDL) and Guidance, Navigation, and Control (GN&C) engineers. Throughout the years, these challenges have been met with a series of landing architectures that spawned different degrees of passive and active control, from the ballistic airbag landers in the Mars Pathfinder and Spirit and Opportunity Rovers, to the guided-entry, SkyCrane-delivered Curiosity. Landing on Europa (the Jovian moon that may have the conditions to harbor life) presents a different set of challenges over a Martian landing. While Europa’s lack of atmosphere relieves the landing engineer from the complexities of heatshields and parachutes and the vagaries of an atmosphere, they now face the enormous challenges of bringing large amounts of fuel and powerful propulsion to do the job the atmosphere does on Mars, while dealing with an extremely uncertain surface topography and radiation environment. These challenges are being addressed with increased automation based on new GN&C sensors and algorithms. In this talk, I will describe the challenges of both Mars and Europa landings, and the intellectual journey trod by engineers in meeting them.
