Institute for Systems Research Technical Reports
Permanent URI for this collectionhttp://hdl.handle.net/1903/4376
This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.
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Item Bio-Inspired Cooperative Optimal Control with Partially-Constrained Final State(2005) Shao, Cheng; Hristu-Varsakelis, Dimitrios; Hristu-Varsakelis, Dimitrios; ISRInspired by the process by which ants gradually optimize their foraging trails, this report investigates the cooperative solution of a class of free-final time, partially-constrained final state optimal control problems by a group of dynamic systems. A class of cooperative, pursuit-based algorithms are proposed for finding optimal solutions by iteratively optimizing an initial feasible control. The proposed algorithms require only short-range, limited interactions between group members, avoid the need for a ``global map'' of the environment on which the group evolves, and solve an optimal control problem in ``small'' pieces, in a manner which will be made precise. The performance of the algorithms is illustrated in a series of simulations and laboratory experiments.Item Local Pursuit as a Bio-Inspired Computational Optimal Control Tool(2005) Shao, Cheng; Hristu-Varsakelis, Dimitrios; Hristu-Varsakelis, Dimitrios; ISR; CDCSSThis paper explores the use of a bio-inspired control algorithm, termed ``local pursuit', as a numerical tool for computing optimal control-trajectory pairs in settings where analytical solutions are difficult to obtain. Inspired by the foraging activities of ant colonies, local pursuit has been the focus of recent work on cooperative optimization. It allows a group of agents to solve a broad class of optimal control problems (including fixed final time, partially-constrained final state problems) and affords certain benefits with respect to the amount of information (description of the environment, coordinate systems, etc.) required to solve the problem. Here, we present a numerical optimization method that combines local pursuit with the well-known technique of multiple shooting, and compare the computational efficiency and capabilities of the two approaches. The proposed method method can overcome some important limitations of multiple shooting by solving an optimal control problem ``in small pieces'. Specifically, the use of local pursuit increases the size of the problem that can be handled under a fixed set of computational resources. Furthermore, local pursuit can be effective in some situations where multiple shooting leads to an ill-conditioned nonlinear programming problem. The trade-off is an increase in computation time. We compare our pursuit-based method with direct multiple shooting using an example that involves optimal orbit transfer of a simple satellite.Item Stabilization of Networked Control Systems under Feedback-based Communication(2004) Zhang, Lei; Hristu-Varsakelis, Dimitrios; Hristu-Varsakelis, Dimitrios; ISRWe study the stabilization of a networked control system (NCS) in which multiple sensors and actuators of a physical plant share a communication medium to exchange information with a remote controller. The plant's sensors and actuators are allowed only limited access to the controller at any one time, in a way that is decided on-line using a feedback-based communication policy. Our NCS model departs from those in previous formulations in that the controller and plant handle communication disruptions by ``ignoring'' (in a sense that will be made precise) sensors and actuators that are not actively communicating. We present an algorithm that provides a complete and straightforward method for simultaneously determining stabilizing gains and communication policies and avoids the computational complexity and limitations associated with some previously proposed models. We introduce three feedback-based scheduling policies that quadratically stabilize the closed-loop NCS while achieving various objectives related to the system's rate of convergence, the priorities of different sensors and actuators, and the avoidance of chattering.Item Biologically-Inspired Optimal Control via Intermittent Cooperation(2004) Shao, Cheng; Hristu-Varsakelis, Dimitrios; ISR; CDCSSWe investigate the solution of a large class of fixed-final-state optimal control problems by a group of cooperating dynamical systems. We present a pursuit-based algorithm -- inspired by the foraging behavior of ants -- that requires each system-member of the group to solve a finite number of optimization problems as it follows other members of the group from a starting to a final state. Our algorithm, termed "sampled local pursuit", is iterative and leads the group to a locally optimal solution, starting from an initial feasible trajectory. The proposed algorithm is broad in its applicability and generalizes previous results; it requires only short-range sensing and limited interactions between group members, and avoids the need for a "global map" of the environment or manifold on which the group evolves. We include simulations that illustrate the performance of our algorithm.Item Optimal Control through Biologically-Inspired Pursuit(2004) Shao, Cheng; Hristu-Varsakelis, Dimitrios; ISR; CDCSSInspired by the process by which ants gradually optimize their foraging trails, this paper investigates the cooperative solution of a class of free-final time, partially-constrained final state optimal control problems by a group of dynamic systems. A cooperative, pursuit-based algorithm is proposed for finding optimal solutions by iteratively optimizing an initial feasible control. The proposed algorithm requires only short-range, limited interactions between group members, and avoids the need for a "global map" of the environment on which the group evolves. The performance of the algorithm is illustrated in a series of numerical experiments.Item Stabilization of Networked Control Systems: Designing Effective Communication Sequences(2004) Zhang, Lei; Hristu-Varsakelis, Dimitrios; Hristu-Varsakelis, Dimitrios; ISRThis paper discusses the stabilization of a networked control system (NCS) whose sensors and actuators exchange information with a remote controller over a shared communication medium. Access to that medium is governed by a pair of periodic communication sequences. Under the model utilized here, the controller and plant handle communication disruptions by ``ignoring' (in a sense to be made precise) sensors and actuators that are not actively communicating. It is shown that under mild conditions, there exist periodic communication sequences that preserve the reachability and observability of the plant, leading to a straightforward design of a stabilizing feedback controller.Item Biologically Inspired Algorithms for Optimal Control(2004) Shao, Cheng; Hristu-Varsakelis, Dimitrios; ISR; CDCSSIn the past few years, efforts to codify the organizing principles behind biological systems have been capturing the attention of a growing number of researchers in the systems and control community. This endeavor becomes increasingly important as new technologies make it possible to engineer complex cooperating systems that are nevertheless faced with many of the challenges long-overcome by their natural counterparts. One area in particular where biology serves as an inspiring but still distant example, involves systems in which members of a species cooperate to form collectives whose abilities are beyond those of individuals. This paper looks to the process by which ants optimize their foraging trails as inspiration for an organizing principle by which groups of dynamical systems can solve a class of optimal control problems. We explore the use of a strategy termed `local pursuit', which allows members of the group to overcome their limitations with respect to sensing range and available information through the use of neighbor-to-neighbor interactions. Local pursuit enables the group to find an optimal solution by iteratively improving upon an initial feasible control. We show that our proposed strategy subsumes previous pursuit-based models for ant-trail optimization and applies to a large array of problems, including many of the classical situations in optimal control. The performance of our algorithm is illustrated in a series of numerical experiments. Ongoing work directions related to local pursuit are also discussed in this document.Item Stochastic Language-based Motion Control(2003) Andersson, Sean B.; Hristu-Varsakelis, Dimitrios; ISR; CDCSSIn this work we present an efficient environment representation based on the use of landmarks and language-based motion programs. The approach is targeted towards applications involving expansive, imprecisely known terrain without a single global map. To handle the uncertainty inherent in real-world applications a partially-observed controlled Markov chain structure is used in which the state space is the set of landmarks and the control space is a set of motion programs. Using dynamic programming, we derive an optimal controller to maximize the probability of arriving at a desired landmark after a finite number of steps. A simple simulation is presented to illustrate the approach.Item On the Structural Complexity of the Motion Description Language MDLe(2003) Hristu-Varsakelis, Dimitrios; Egerstedt, Magnus; Krishnaprasad, Perinkulam S.; ISRAs modern control theory attempts to elucidate the complexity of systems that combine differential equations and event-driven logic, it must overcome challenges having to do with limited expressive power as well as practical difficulties associated with translating control algorithms into robust and reusable software. The Motion Description Language (MDL) and its ``extended' counterpart MDLe, have been at the center of an ongoing effort to make progress on both of these fronts. The goal of this paper is to define MDLe as a formal language, thereby connecting with the vast literature on the subject, and to stimulate experimental work. We discuss the expressive power of MDLe and provide some examples of MDLe programs.This work has appeared in the Proceedings of the 41st IEEE Conference on Decision and Control, 2002.
Item Interrupt-based feedback control over a shared communication medium(2003) Hristu-Varsakelis, Dimitrios; Kumar, P. R.; ISRThis work is a continuation of recent efforts aimed at understanding the interplay of control, communication and computation in systems whose sensors, actuators and computing elements are distributed across a network. We investigate the simultaneous stabilization of a group of linear systems whose feedback loops are closed over an idealized shared medium. The capacity of that medium is constrained so that only a limited number of controller-plant connections can be accommodated at any one time. We introduce a feedback communication policy -- inspired by previous work on queuing systems and real-time scheduling -- for deciding which system(s) should be admitted into the network and for how long. The use of feedback in making communication decisions results in a set of autonomous dynamical systems which are coupled to one another due to the presence of communication constraints. We give conditions for the stability of the collection under the proposed communication policy and present simulation results that illustrate our ideas.This paper appeared in the Proceedings of the 42st IEEE Conference on Decision and Control, 2002.