A textbook for a graduate course for applied mathematics and engineering students that presents linear optimal systems and stochastic estimation and control within a unified framework. A selection of topics blends theoretical foundations, analysis and design methodologies, and application-oriented tools. An elementary course in control is recommended; one in probability and stochastic processes is a prerequisite. Annotation copyright Book News, Inc. Portland, Or.
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Key Benefit: Using a common unifying framework, this volume explores the main topics of Linear Quadratic control, predictive control, and adaptive predictive control -- in terms of theoretical foundations, analysis and design methodologies, and application-orient ed tools. Key Topic: Presents LQ and LQG control via two alternative approaches: the Dynamic Programming (DP) and the Polynomial Equation (PE) approach. Discusses predicable control, an important tool in industrial applications, within the framework of LQ control, and presents innovative predictive control schemes having guaranteed stability properties. Offers a unique, thorough presentation of indirect adaptive multi-step predictive controllers, with detailed proofs of globally convergent schemes for both the ideal and the bounded disturbance case. Extends the self-tuning property of one-step-ahead control to multi-step control. Market: For engineers and mathematicians interested in the theory, analysis and design methodologies, and application-oriented tools of optimal, predictive and adaptive control.
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