A universal adaptive stabilizer for a class of nonlinear systems
Systems & Control Letters
Stable Adaptive Neural Network Control
Stable Adaptive Neural Network Control
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
A combined backstepping and small-gain approach to robust adaptive fuzzy output feedback control
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
IEEE Transactions on Fuzzy Systems
Automatica (Journal of IFAC)
Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Design of Robust Adaptive Controllers for Nonlinear Systems with Dynamic Uncertainties
Automatica (Journal of IFAC)
Brief A combined backstepping and small-gain approach to adaptive output feedback control
Automatica (Journal of IFAC)
Adaptive neural network control for a class of low-triangular-structured nonlinear systems
IEEE Transactions on Neural Networks
Gaussian networks for direct adaptive control
IEEE Transactions on Neural Networks
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In this paper, robust adaptive control is proposed for a class of pure-feedback nonlinear systems with unmodeled dynamics and unknown gain signs using radial basis function neural networks (RBFNNs). Dynamic uncertainties are dealt with using a dynamic signal. The unknown virtual gain signs are solved using the Nussbaum functions. Using mean value theorem and Young's inequality, only one learning parameter needs to be tuned online at each step of recursion. It is proved that the proposed design scheme can guarantee semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system. Simulation results demonstrate the effectiveness of the proposed approach.