Nonlinear and Adaptive Control Design
Nonlinear and Adaptive Control Design
Stable Adaptive Neural Network Control
Stable Adaptive Neural Network Control
Approximation-based control of nonlinear MIMO time-delay systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Information Sciences: an International Journal
Information Sciences: an International Journal
Adaptive neural network control of nonlinear systems by state andoutput feedback
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive neural network control for strict-feedback nonlinear systems using backstepping design
Automatica (Journal of IFAC)
Robust adaptive control of nonlinear systems with unknown time delays
Automatica (Journal of IFAC)
Gaussian networks for direct adaptive control
IEEE Transactions on Neural Networks
International Journal of Automation and Computing
International Journal of Automation and Computing
International Journal of Automation and Computing
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In this paper, adaptive neural tracking control is proposed based on radial basis function neural networks (RBFNNs) for a class of multi-input multi-output (MIMO) nonlinear systems with completely unknown control directions, unknown dynamic disturbances, unmodeled dynamics, and uncertainties with time-varying delay. Using the Nussbaum function properties, the unknown control directions are dealt with. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown upper bound functions of the time-varying delay uncertainties are compensated. The proposed control scheme does not need to calculate the integral of the delayed state functions. Using Young's inequality and RBFNNs, the assumption of unmodeled dynamics is relaxed. By theoretical analysis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded.