An extended direct scheme for robust adaptive nonlinear control
Automatica (Journal of IFAC)
Feedback linearization using neural networks
Automatica (Journal of IFAC)
Variable neural networks for adaptive control of nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Brief Adaptive control of nonlinearly parameterized systems with a triangular structure
Automatica (Journal of IFAC)
Output feedback control of nonlinear systems using RBF neural networks
IEEE Transactions on Neural Networks
Approximation of nonlinear systems with radial basis function neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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The problem of output tracking control for a class of multi-input multi-output uncertain systems is considered. A novel adaptive robust controller is proposed, which incorporates a variable-structure radial basis function (RBF) network to approximate unknown system dynamics. The RBF network can determine its structure on-line dynamically, where radial basis functions are added or removed to ensure the desired tracking accuracy and to prevent the network redundancy simultaneously. The structure variation of the RBF network is taken into account in the stability analysis of the closed-loop system. This is accomplished by using the piecewise continuous quadratic Lyapunov function typically for the analysis of switched and hybrid systems.