Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties. Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control methods in more complex systems.

Borrowing from Biology

Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts.

Progressive Development

After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware.

Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.

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. Annotation ©2007 Book News, Inc., Portland, OR (booknews.com)

Exploring controller design using artificial neural networks, Neural Network Control of Nonlinear Discrete-Time Systems builds the necessary background in neural networks, dynamical systems, stability theory, and feedback linearization of nonlinear discrete-time systems. The authors develops a framework for implementing intelligent control systems on actual systems using embedded computer hardware. The presentation includes stability proofs, simulation examples, and appendices with computer codes for building controllers for nonlinear systems and real-time control applications.

Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware.