Advanced Model Predictive Control
by Tao Zheng
Publisher: InTech 2011
Number of pages: 418
Model Predictive Control (MPC) refers to a class of control algorithms in which a dynamic process model is used to predict and optimize process performance. From lower request of modeling accuracy and robustness to complicated process plants, MPC has been widely accepted in many practical fields.
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by M. H. A. Davis - Tata Institute of Fundamental Research
There are actually two separate series of lectures, on controlled stochastic jump processes and nonlinear filtering respectively. They are united however, by the common philosophy of treating Markov processes by methods of stochastic calculus.
by Andrew Whitworth - Wikibooks
An inter-disciplinary engineering text that analyzes the effects and interactions of mathematical systems. This book is for third and fourth year undergraduates in an engineering program. It considers both classical and modern control methods.
by Ivan Ganchev Ivanov (ed.) - InTech
The book provides a self-contained treatment on practical aspects of stochastic modeling and calculus including applications in engineering, statistics and computer science. Readers should be familiar with probability theory and stochastic calculus.
by Shankar Sastry, Marc Bodson - Prentice Hall
The book gives the major results, techniques of analysis and new directions in adaptive systems. It presents deterministic theory of identification and adaptive control. The focus is on linear, continuous time, single-input single output systems.