CDC2000 Control of Markov Processes

Control of Markov Processes
Home Full List of Titles 1: Proceedings of CDC2000 Discrete Event Systems Control in Communication Systems Optimal Control and Applications I Optimisation Approaches and Methods Model Predictive Control Advances in Linear Estimation Stochastic and Uncertain Systems Nonlinear Control and Applications Nonlinear Estimation and Filtering Formation Control and its Applications New Approaches to Fuzzy Control Manufacturing Systems Automotive Applications Stability Issues in Hybrid Control Recent Advances in Stochastic Networks Optimal Control and Applications II Robust Controller Design - mu, L1 and H2 Constrained and Receding Horizon Control Identification and Control around the World Markov Decision Processes Nonlinear Optimisation Observers for Nonlinear Systems Motion Planning Neural / Fuzzy Stability and Control Motor Control Control of Quantum Phenomena I Hybrid Systems Methods Control in Communication Networks Robustness and Optimisation Bumpless Transfer, Antiwindup and Saturation 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Control and Stabilisation I Tracking Vision Servoing Controllability of Nonlinear Systems Control of Flexible Systems Electro-Mechanical Systems Robust Control Methods and Applications Fault Detection and Diagnosis Optimisation and Applications Robust Stability Analysis Numerical Methods in Control Filtering in Continuous Time Stochastic Systems Interplay between Control and Signal Processing Fault Detection and Analysis Nonlinear Dynamical Systems Nonlinear Time Delay Systems Computational Issues in Nonlinear Control Disturbance Rejection Process Control Industry Applications Linear Parameter Varying Systems Linear Control Systems Dynamic and Nonlinear Programming Model Reduction Applications New Techniques for Control and Systems: Numerical Linear Algebra Estimation and Identification using Hidden Markov Models Applications of Stochastic Control Topics in Linear Design Nonlinear Control and Stabilisation II Ambulatory Robot Systems Chaotic and Oscillatory Systems Biomedical System Control Integrated Control and CPU Scheduling Linear Design Techniques Adaptive Disturbance / Noise Compensation Nonlinear Model Predictive Control Sensitivity Design, Analysis and Limitations Analysis of Linear Systems Linear Matrix Inequalities in Design Lyapunov's 2nd Method Robotics: Tracking Control Lagrangian and Hamiltonian Theory Variable Structure Control Machine Vision Signal Processing Methods in Control Applied Nonlinear Control Author Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z	Stability Of Digital Control Systems Subject To Jump Linear Random Perturbations Authors: W. Steven Gray, Oscar R. Gonzalez, Sudarshan Patilkulkarni, Volume: 1, Page 1154 Paper number 1229 Abstract: In a number of applications involving fault tolerant digital control systems, there naturally arises a class of jump linear discrete-time systems characterized by having random perturbations in their drift terms. In this paper, a necessary and sufficient condition for mean square stability of such systems is developed and then applied to the stability analysis of digital flight control systems operating in electromagnetic (EM) environments. In particular, the stability degradation due to EM induced digital memory errors is examined. CD001229.PDF (From Author) TOP H-Infinity Control for Continuous-Time Linear Systems with Infinite Markov Jump Parameters via Semigroup Authors: Marcelo D. Fragoso, Eulina C.S. Nascimento, Jack Baczynski, Volume: 1, Page 1160 Paper number 1737 Abstract: We examine the H-infinity control problem for a class of continuous time linear systems subject to Markovian jumps in the parameters(LSMJP). We extend the results available in two directions: we give necessary and sufficient conditions for the existence of a feedback control which stochastically stabilizes the LSMJP and ensures that a certain L2 induced norm be less than a prespecified value. In addition, we consider the case in which the state-space of the Markov chain takes value in a countably infinite set. The solution here is given in terms of a countably infinite set of coupled algebraic Riccati equations(ICARE). In this scenario, many subtleties come up and therefore require the use of new techniques. For instance, we had to frame the problem into the context of infinite dimensional Banach space and use tools from semigroup theory, as well as a decomplexification technique. Finally, a powerful operator result of Yakubovick and a result, derived by the authors, that bounds up stochastic stability with the spectrum of an infinity dimensional Banach space operator, team up in the proof of the main theorem. CD001737.PDF (From Author) TOP Sequential Simulation-Based Estimation Of Jump Markov Linear Systems Authors: Arnaud Doucet, Neil J. Gordon, Vikram Krishnamurthy, Volume: 1, Page 1166 Paper number 1350 Abstract: Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. Our aim is to recursively compute optimal conditional mean state estimates for JMLS. We present efficient simulation-based algorithms called particle filters to solve the optimal filtering problem. Our algorithms combine sequential importance sampling, a selection scheme and Markov chain Monte Carlo methods. They use several variance reduction methods to make the most of the statistical structure of JMLS. CD001350.PDF (From Author) TOP Sample-Path And Variance Minimization Of Markov Control Processes With Average Cost Criteria Authors: Onésimo Hernández-Lerma, Oscar Vega-Amaya, Guadalupe Carrasco, Volume: 1, Page 1172 Paper number 14 Abstract: This paper studies several average costs criteria for Markov control processes on Borel spaces with possibly unbounded costs. Under suitable hypotheses it is shown; (i) the existence of a sample-path average cost (SPAC-)optimal stationary policy; (ii) a stationary policy is SPAC-optimal if and only if it is expected average cost (EAC-) optimal; and (iii) within the class of stationary SPAC-optimal (equivalently EAC-optimal) there exists one with minimal limiting average variance. CD000014.PDF (From Author) CD000014.PDF (Scanned) TOP Stationary Riccati Equation for Linear Minimum Mean Square Error Estimator of Markovian Jump Systems Authors: Oswaldo L.V. Costa, Susset Guerra Jiménez, Volume: 1, Page 1177 Paper number 1087 Abstract: In this paper we obtain sufficient conditions for the convergence of the error covariance matrix to a stationary value for the linear minimum mean square error estimator (LMMSE) of discrete time linear systems subject to abrupt changes in the parameters modeled by a Markov chain (MJLS). Under the assumption of mean square stability of the MJLS and ergodicity of the associated Markov chain it is shown that there exists a unique solution for the stationary Riccati filter equation, and moreover this solution is the limit of the error covariance matrix of the LMMSE. This result is suitable for designing a time-invariant stable suboptimal filter of LMMSE for MJLS. CD001087.PDF (From Author) TOP Monte Carlo TD((lambda))-Methods for the Optimal Control of Discrete-Time Markovian Jump Linear Systems Authors: Oswaldo Luiz Valle Costa, Julio C.C. Aya, Volume: 1, Page 1183 Paper number 1085 Abstract: In this paper we present an iterative technique based on Monte Carlo simulations for deriving the optimal control of the infinite horizon linear regulator problem of discrete-time Markovian jump linear systems for the case in which the transition probability matrix of the Markov chain is not known. It is well known that the optimal control of this problem is given in terms of the maximal solution of a set of coupled algebraic Riccati equations (CARE), which have been extensively studied over the last few years. We trace a parallel with the theory of TD((lambda)) algorithms for Markovian decision processes to develop a TD((lambda)) like algorithm for the optimal control associated to the maximal solution of the CARE. Some numerical examples are also presented. CD001085.PDF (From Author) TOP