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A neural-network-based adaptive variable structure control methodology is proposed for the tracking problem of nonlinear discrete-time input-output systems. The unknown dynamics of the system are approximated via radial basis function neural networks. The control law is based on sliding modes and simple to implement. The discrete-time adaptive law for tuning the weight of neural networks is presented using the adaptive filtering algorithm with residue upper-bound compensation. The application of the proposed controller to engine idle speed control design is discussed. The results indicate the validation and effectiveness of this approach.
A neural-network-based adaptive variable structure control methodology is proposed for the tracking problem of nonlinear discrete-time input-output systems. The unknown dynamics of the system are approximated via radial basis function neural networks. The control law is based on sliding modes The simple-time adaptive law for tuning the weight of neural networks is presented using the adaptive filtering algorithm with residue upper-bound compensation. The application of the proposed controller to engine idle speed control design is discussed. the validation and effectiveness of this approach.