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115
Distributed adaptive constrained optimization for smart matter systems
 In Proceedings of AAAI Spring Symposium on Intelligent Embedded and Distributed Systems
, 2002
"... The remarkable increase in computing power together with a similar increase in sensor and actuator capabilities now under way is enabling a significant change in how systems can sense and manipulate their environment. These changes require control algorithms capable of operating a multitude of inter ..."
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The remarkable increase in computing power together with a similar increase in sensor and actuator capabilities now under way is enabling a significant change in how systems can sense and manipulate their environment. These changes require control algorithms capable of operating a multitude of interconnected components. In particular, novel “smart matter” systems will eventually use thousands of embedded, microsize sensors, actuators and processors. In this paper, we propose a new framework for a online, adaptive constrained optimization for distributed embedded applications. In this approach, online optimization problems are decomposed and distributed across the network, and solvers are controlled by an adaptive feedback mechanism that guarantees timely solutions. We also present examples from our experience in implementing smart matter systems to motivate our ideas.
A framework for online adaptive control of problem solving
 In Proc. of CP2001 workshop on OnLine combinatorial problem solving and Constraint Programming, Paphos
, 2001
"... The design of a problem solver for a particular problem depends on the problem type, the system resources, and the application requirements, as well as the specific problem instance. The difficulty in matching a solver to a problem can be ameliorated through the use of online adaptive control of sol ..."
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The design of a problem solver for a particular problem depends on the problem type, the system resources, and the application requirements, as well as the specific problem instance. The difficulty in matching a solver to a problem can be ameliorated through the use of online adaptive control of solving. In this approach, the solver or problem representation selection and parameters are defined appropriately to the problem structure, environment models, and dynamic performance information, and the rules or model underlying this decision are adapted dynamically. This paper presents a general framework for the adaptive control of solving and discusses the relationship of this framework both to adaptive techniques in control theory and to the existing adaptive solving literature. Experimental examples are presented to illustrate the possible uses of solver control. 1
Gramianbased model reduction for datasparse systems
, 2007
"... Model reduction is a common theme within the simulation, control and optimization of complex dynamical systems. For instance, in control problems for partial differential equations, the associated largescale systems have to be solved very often. To attack these problems in reasonable time it is abs ..."
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Model reduction is a common theme within the simulation, control and optimization of complex dynamical systems. For instance, in control problems for partial differential equations, the associated largescale systems have to be solved very often. To attack these problems in reasonable time it is absolutely necessary to reduce the dimension of the underlying system. We focus on model reduction by balanced truncation where a system theoretical background provides some desirable properties of the reducedorder system. The major computational task in balanced truncation is the solution of largescale Lyapunov equations, thus the method is of limited use for really largescale applications. We develop an effective implementation of balancingrelated model reduction methods in exploiting the structure of the underlying problem. This is done by a datasparse approximation of the largescale state matrix A using the hierarchical matrix format. Furthermore, we integrate
Hybrid symbolic and numerical simulation studies of timefractional order wavediffusion systems
 International Journal of Control
, 2006
"... Boundary control of timefractional order diffusionwave systems is becoming an active research area. However, there is no readily available simulation tool till now for researchers to analyze and design controllers. In this paper, a simulation method for some typical boundary control problems, comb ..."
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Boundary control of timefractional order diffusionwave systems is becoming an active research area. However, there is no readily available simulation tool till now for researchers to analyze and design controllers. In this paper, a simulation method for some typical boundary control problems, combining symbolic mathematics and numerical method, is presented with two application examples. In the intermediate steps of the simulation, an important byproduct, the transfer function of the controlled system, can be obtained, which makes the design of more advanced boundary controllers possible and much easier. 1.
Adaptive control of MEMS devices
 Proceedings of the Conference for Intelligent Systems and Control (August 14
"... The manufacturing of MicroElectroMechanical Systems (MEMS) involves several micromachining processes, each include variations in the parameters of the device from the desired values. These variations change the behavior of the device and affect its performance, therefore there is a need of means ..."
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The manufacturing of MicroElectroMechanical Systems (MEMS) involves several micromachining processes, each include variations in the parameters of the device from the desired values. These variations change the behavior of the device and affect its performance, therefore there is a need of means to compensate these changes and control the device’s behavior. In this paper, some manufacturing steps that result in uncertainties in modeling of the device are investigated. An effective method to control their behavior is introduced and is also experimentally tested. In this regard, application of the continuoustime Model Reference Adaptive Controllers (MRAC) for tracking control of micro comb resonators is investigated. The procedure of the controller design and modeling of micro comb resonator considering the uncertainties are investigated. The controller is implemented in a realtime control board and the experimental results demonstrate the capabilities of MRAC in a multiuncertainty MEMS motion control task. KEY WORDS Micro comb resonator, modeling, parameter uncertainty, adaptive control, tracking
Evolution of Cooperation in Arbitrary Complex Networks
 In 13th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS
, 2014
"... This paper proposes a new model, based on the theory of nonlinear dynamical systems, to study the evolution of cooperation in arbitrary complex networks. We consider a large population of agents placed on some arbitrary network, interacting with their neighbors while trying to optimize their fitnes ..."
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This paper proposes a new model, based on the theory of nonlinear dynamical systems, to study the evolution of cooperation in arbitrary complex networks. We consider a large population of agents placed on some arbitrary network, interacting with their neighbors while trying to optimize their fitness over time. Each agent’s strategy is continuous in nature, ranging from purely cooperative to purely defective behavior, where cooperation is costly but leads to shared benefits among the agent’s neighbors. This induces a dilemma between social welfare and individual rationality. We show in simulation that our model clarifies why cooperation prevails in various regular and scalefree networks. Moreover we observe a relation between the network size and connectivity on the one hand, and the resulting level of cooperation in equilibrium on the other hand. These empirical findings are accompanied by an analytical study of stability of arbitrary networks. Furthermore, in the special case of regular networks we prove convergence to a specific equilibrium where all agents adopt the same strategy. Studying under which scenarios cooperation can prevail in structured societies of selfinterested individuals has been a topic of interest in the past two decades. However, related work has been mainly restricted to either analytically studying a specific network structure, or empirically comparing different network structures. To the best of our knowledge we are the first to propose a dynamical model that can be used to analytically study arbitrary complex networks.
SEEC: A Framework for Selfaware Computing
, 2010
"... As the complexity of computing systems increases, application programmers must be experts in their application domain and have the systems knowledge required to address the problems that arise from parallelism, power, energy, and reliability concerns. One approach to relieving this burden is to make ..."
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As the complexity of computing systems increases, application programmers must be experts in their application domain and have the systems knowledge required to address the problems that arise from parallelism, power, energy, and reliability concerns. One approach to relieving this burden is to make use of selfaware computing systems, which automatically adjust their behavior to help applications achieve their goals. This paper presents the SEEC framework, a unified computational model designed to enable selfaware computing in both applications and system software. In the SEEC model, applications specify goals, system software specifies possible actions, and the SEEC framework is responsible for deciding how to use the available actions to meet the applicationspecified goals. The SEEC framework is built around a general and extensible control system which provides predictable behavior and allows SEEC to make decisions that achieve goals while optimizing resource utilization. To demonstrate the applicability of the SEEC framework, this paper presents five different selfaware systems built using SEEC. Case studies demonstrate how these systems can control the performance of the PARSEC benchmarks, optimize performance per Watt for a video encoder, and respond to unexpected changes in the underlying environment. In general these studies demonstrate that systems built using the SEEC framework are goaloriented, predictable, adaptive, and extensible. 1.
Qualitative Heterogeneous Control of Higher Order Systems
 HYBRID SYSTEMS: COMPUTATION AND CONTROL
, 2003
"... This paper presents the qualitative heterogeneous control framework, a methodology for the design of a controlled hybrid system based on attractors and transitions between them. This framework designs a robust controller that can accommodate bounded amounts of parametric and structural uncertaint ..."
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This paper presents the qualitative heterogeneous control framework, a methodology for the design of a controlled hybrid system based on attractors and transitions between them. This framework designs a robust controller that can accommodate bounded amounts of parametric and structural uncertainty. This framework provides a number of advantages over other similar techniques. The local models used in the design process are qualitative, allowing the use of partial knowledge about system structure, and nonlinear, allowing regions and transitions to be defined in terms of dynamical attractors. In addition, we define boundaries between local models in a natural manner, appealing to intrinsic properties of the system. We demonstrate the use of this framework by designing a novel control algorithm for the cartpole system. In addition, we illustrate how traditional algorithms, such as linear quadratic regulators, can be incorporated within this framework. The design is validated by experiments with a physical system.
Control Theory
 In Leslie Hogben (Hrsg.), Handbook of Linear Algeba, Kapitel 57, Chapman & Hall/CRC
, 2006
"... Given a dynamical system described by the ordinary differential equation (ODE) ˙x(t) = f(t,x(t),u(t)), x(t0) = x 0, where x is the state of system and u serves as input, the major problem in control theory is to steer the state from x 0 to some desired state, i.e., for a given initial value x(t0) ..."
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Given a dynamical system described by the ordinary differential equation (ODE) ˙x(t) = f(t,x(t),u(t)), x(t0) = x 0, where x is the state of system and u serves as input, the major problem in control theory is to steer the state from x 0 to some desired state, i.e., for a given initial value x(t0) = x 0 and target x 1, can we find a piecewise continuous or L2 (i.e., squareintegrable, Lebesgue measurable) control function û such that there exists t1 ≥ t0 with x(t1;û) = x 1 where x(t;û) is the solution trajectory of the ODE given above for u ≡ û? Often, the target is x 1 = 0, in particular if x describes the deviation from a nominal path. A weaker demand is to asymptotically stabilize the system, i.e., to find an admissible control function û (i.e., a piecewise continuous or L2 function û: [t0,t1] ↦ → U) such that limt→ ∞ x(t;û) = 0. Another major problem in control theory arises from the fact that often, not all states are available for measurements or observations. Thus we are faced with the question: given partial information about the states, is it possible to reconstruct the solution trajectory from the measurements/observations? If this is the case, the states can be estimated by state observers. The classical approach leads to the Luenberger observer, but nowadays most frequently the
Heuristic Search in Infinite State Spaces Guided by Lyapunov Analysis
 Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence
, 2001
"... In infinite state spaces, many standard heuristic search algorithms do not terminate if the problem is unsolvable. Under some conditions, they can fail to terminate even when there are solutions. We show how techniques from control theory, in particular Lyapunov stability analysis, can be emplo ..."
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In infinite state spaces, many standard heuristic search algorithms do not terminate if the problem is unsolvable. Under some conditions, they can fail to terminate even when there are solutions. We show how techniques from control theory, in particular Lyapunov stability analysis, can be employed to prove the existence of solution paths and provide guarantees that search algorithms will find those solutions. We study both optimal search algorithms, such as A*, and suboptimal/realtime search methods. A Lyapunov framework is useful for analyzing infinitestate search problems, and provides guidance for formulating search problems so that they become tractable for heuristic search. We illustrate these ideas with experiments using a simulated robot arm. 1