Neuro-fuzzy control of industrial systems with actuator nonlinearities
F. L. Lewis, J. Campos, R. Selmic
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. Neural networks capture the parallel processing and learning capabilities of biological nervous systems, and fuzzy logic captures the decision-making capabilities of human linguistics and cognitive systems.
Audience This book is written for students in a college curriculum, for practicing engineers in industry, and for university researchers.
سب زمرہ:
سال:
2002
ناشر کتب:
Society for Industrial and Applied Mathematics
زبان:
english
صفحات:
259
ISBN 10:
0898715059
ISBN 13:
9780898715057
سیریز:
Frontiers in applied mathematics 24
فائل:
DJVU, 2.02 MB
IPFS:
,
english, 2002
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