Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities. This item is unavailable.Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities

Neural networks and fuzzy systems are model free control design approaches that represent an advantage over classical control when dealing with complicated nonlinear actuator dynamics. Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities brings neural networks and fuzzy logic together with dynamical control systems. Each chapter presents powerful control approaches for the design of intelligent controllers to compensate for actuator nonlinearities such as time delay, friction, deadzone, and backlash that can be found in all industrial motion systems, plus a thorough development, rigorous stability proofs, and simulation examples for each design. In the final chapter, the authors develop a framework to implement intelligent control schemes on actual systems. Rigorous stability proofs are further verified by computer simulations, and appendices contain the computer code needed to build intelligent controllers for real-time applications.

Frank L. Lewis is Associate Director for Research and Head of the Advanced Controls, Sensors, and MEMS Group at the Automation and Robotics Research Institute at the University of Texas at Arlington.
Javier Campos is Linux Consultant Engineer at Montavista Software, Inc. in Irving, Texas.
Rastko Selmic is DSP Systems Engineer at Signalogic, Inc. in Dallas, Texas.