Neural Network Control of Nonliner Discrete-Time Systems.
Neural network control of nonliner discrete-time systems.
CRC / Taylor & Francis
Control engineering; v.21
The increasing complexity of aerospace engineering, automotive technology, military, and industrial systems have rendered traditional feedback control systems increasingly less able to meet desired performance requirements, thus sparking interest in intelligent control systems using artificial neural networks, fuzzy logic, and genetic algorithms. In this book, Sarangapani (U. of Missouri) describes controller design in discrete-time using artificial neural networks (NN) since they "capture the parallel processing, adaptive, and learning capabilities of biological nervous systems." After providing the background on neural networks and discrete-time adaptive control, he presents chapters discussing neural network control of nonlinear systems and feedback linearization, neural network control of uncertain nonlinear discrete-time systems with actuator nonlinearities, output feedback control of strict feedback nonlinear multiple input/multiple output discrete-time systems, neural network control of nonstrict feedback nonlinear systems, system identification using discrete-time neural networks, discrete-time model reference adaptive control, neural network control in discrete-time using Hamilton-Jacobi-Bellman formulation, and neural network output feedback controller design and embedded hardware implementation.
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|Article Type:||Brief Article|
|Date:||Mar 1, 2007|
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