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.
Home page url
Download or read it online for free here:
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.
by Roy D. Byrd - Delmar Publishers
These materials are intended to provide a meaningful experience with automatic controls for students of modern technology. The topics included provide exposure to basic principles of control systems, transducers, actuators, amplifiers, controllers.
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 Jean-Michel Coron - American Mathematical Society
This book presents methods to study the controllability and the stabilization of nonlinear control systems in finite and infinite dimensions. Examples are given where nonlinearities turn out to be essential to get controllability or stabilization.