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Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities

F. L. LewisJ. CamposR. Selmic

出版	Society for Industrial and Applied Mathematics, 2002
主題	Computers / Data Science / Neural NetworksMathematics / GeneralMathematics / AppliedMathematics / Linear & Nonlinear ProgrammingMathematics / Mathematical AnalysisScience / System TheoryTechnology & Engineering / GeneralTechnology & Engineering / Engineering (General)Technology & Engineering / Robotics
ISBN	08987150599780898715057
URL	http://books.google.com.hk/books?id=G7dsPNe6JAEC&hl=&source=gbs_api

註釋

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.