Neural Network Based Controller for Constrained Multivariable Systems
نویسنده
چکیده
This paper presents a new neural network based controller design for multivariable systems. The proposed controller is designed using radial basis function (RBF) neural network. Weight update equation using classical least mean square principle is derived for the RBF network. The controller generates optimal control signals abiding by constraints, if any, on the control signals. Simulation results are included in the end to assess the performance of the proposed controller. Key–Words: Neural Network, Radial Basis Functions, Optimization, Multivariable, Constraints.
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