Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode |
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E-Books | Biblioteca da FCTUNL Online | Não Ficção | QA402.3. SPR FCT 94755 (Browse shelf) | 1 | Available |
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QA402.3.SPR FCT 82401 Controllability of partial differential equations governed by multiplicative controls | QA402.3.SPR FCT 82723 Optimization and control techniques and applications | QA402.3.SPR FCT 94108 Fourier series in control theory | QA402.3. SPR FCT 94755 Mathematical methods in robust control of linear stochastic systems | QA402.3. SPR FCT 94777 Uniform output regulation of nonlinear systems | QA402.3. SPR FCT 95606 Mathematical control theory | QA402.3.SPR FCT 95985 Geometric optimal control |
Colocação: Online
Linear stochastic systems are successfully used to provide mathematical models for real processes in fields such as aerospace engineering, communications, manufacturing, finance and economy. This monograph presents a useful methodology for the control of such stochastic systems with a focus on robust stabilization in the mean square, linear quadratic control, the disturbance attenuation problem, and robust stabilization with respect to dynamic and parametric uncertainty. Systems with both multiplicative white noise and Markovian jumping are covered. Key Features: -Covers the necessary pre-requisites from probability theory, stochastic processes, stochastic integrals and stochastic differential equations -Includes detailed treatment of the fundamental properties of stochastic systems subjected both to multiplicative white noise and to jump Markovian perturbations -Systematic presentation leads the reader in a natural way to the original results -New theoretical results accompanied by detailed numerical examples -Proposes new numerical algorithms to solve coupled matrix algebraic Riccati equations. The unique monograph is geared to researchers and graduate students in advanced control engineering, applied mathematics, mathematical systems theory and finance. It is also accessible to undergraduate students with a fundamental knowledge in the theory of stochastic systems.
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