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About this product
- DescriptionNeural networks and fuzzy systems are model free control design approaches that represent an advantage over classical control when dealing with complicated nlinear 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 nlinearities 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.
- Author(s)F.L. Lewis,Javier Campos,Rastko Selmic
- PublisherSociety for Industrial & Applied Mathematics,U.S.
- Date of Publication01/01/1987
- SubjectComputing: General
- Series TitleFrontiers in Applied Mathematics
- Series Part/Volume Numberv. 24
- Place of PublicationNew York
- Country of PublicationUnited States
- ImprintSociety for Industrial & Applied Mathematics,U.S.
- Content NoteIllustrations
- Weight635 g
- Width152 mm
- Height228 mm
- Spine17 mm
- Series Edited byH. T. Banks
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