Springer Advanced Topics in Control and Estimation of State-Multiplicative Noisy Systems: 439 (Lecture Notes in Control and Information Sciences, 439)

Advanced Topics in Control and Estimation of State-Multiplicative Noisy Systems begins with an introduction and extensive literature survey. The text proceeds to cover the field of H∞ time-delay linear systems where the issues of stability and L2−gain are presented and solved for nominal and uncertain stochastic systems, via the input-output approach. It presents solutions to the problems of state-feedback, filtering, and measurement-feedback control for these systems, for both the continuous- and the discrete-time settings. In the continuous-time domain, the problems of reduced-order and preview tracking control are also presented and solved. The second part of the monograph concerns non-linear stochastic state- multiplicative systems and covers the issues of stability, control and estimation of the systems in the H∞ sense, for both continuous-time and discrete-time cases. The book also describes special topics such as stochastic switched systems with dwell time and peak-to-peak filtering of nonlinear stochastic systems. The reader is introduced to six practical engineering- oriented examples of noisy state-multiplicative control and filtering problems for linear and nonlinear systems. The book is rounded out by a three-part appendix containing stochastic tools necessary for a proper appreciation of the text: a basic introduction to stochastic control processes, aspects of linear matrix inequality optimization, and MATLAB codes for solving the L2-gain and state-feedback control problems of stochastic switched systems with dwell-time. Advanced Topics in Control and Estimation of State-Multiplicative Noisy Systems will be of interest to engineers engaged in control systems research and development, to graduate students specializing in stochastic control theory, and to applied mathematicians interested in control problems. The reader is expected to have some acquaintance with stochastic control theory and state-space-based optimal control theory andmethods for linear and nonlinear systems.