This book presents algorithms and numerical techniques used for the analysis and control design of stochastic linear systems with multiplicative and additive noise. It also discusses computational techniques for the matrix pseudoinverse in minimum variance reduced-order filtering and control; decomposition technique in multiobjective discrete-time dynamic problems; computational techniques in robotic systems; reduced complexity algorithm using microprocessors; algorithms for image-based tracking; and modeling of linear and nonlinear systems.
This volume will be an important reference source for practitioners in the field who are looking for techniques with significant applied implications.
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