Titlebook: Radial Basis Function (RBF) Neural Network Control for Mechanical Systems; Design, Analysis and Jinkun Liu Book 2013 Tsinghua University Pr

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Radial Basis Function (RBF) Neural Network Control for Mechanical SystemsDesign, Analysis and

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Radial Basis Function (RBF) Neural Network Control for Mechanical Systems978-3-642-34816-7

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Book 2013aptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control d

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Introduction,ol. To illustrate the attendant features of robustness and performance specification of RBF adaptive control, a typical RBF adaptive controller design for an example system is given. A concrete analysis, simulation examples, and Matlab programs are given too.

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Adaptive RBF Neural Network Control, control based on neural approximation with unknown parameter, and a direct robust adaptive control. For above control laws, the adaptive law is designed based on the Lyapunov stability theory, the closed-loop system stability can be achieved.

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Adaptive RBF Control Based on Global Approximation,ol law, adaptive neural network control law with sliding mode robust term, and adaptive neural network control law with HJI. The closed-loop system stability can be achieved based on the Lyapunov stability.

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