Direct control with radial basis function networks: Stability analysis and applications

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Abstract In the present paper, neural control and identification of general nonlinear plants are accomplished using radial basis function (RBF) networks. A neural controller is adjusted oil-line by using the orthogonal least squares (OLS) method. A stability analysis has been performed using the conicity criterion, and based upon this a new training data set is elicited so that a stable neural controller is obtained. Applications both to a nonlinear fluid level system and to an inverted pendulum are detailed, demonstrating the effectiveness of the proposed method. Training time with the OLS method are reduced compared to standard backpropagation technique.