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In this paper, an approximation-based robust adaptive tracking control is developed for a class of uncertain multiple-input and multiple-output (MIMO) nonlinear systems with unknown disturbances and input saturation. Radial basis function neural networks (RBFNNs) are used to approximate the function uncertainties of MIMO nonlinear systems. An auxiliary design system is introduced to analyze the constraint...
An adaptive neural network control strategy for a class of nonlinear system is proposed, which combines the technique in generalized predictive control theory and the gradient descent rule to accelerate learning and improve convergence with neural networkpsilas capability of approximating to nonlinear function, Taking the neural network as a model of the system, control signals are directly obtained...
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