Robust adaptive control
Nonlinear and Adaptive Control Design
Nonlinear and Adaptive Control Design
Adaptive neural network control of nonlinear systems by state andoutput feedback
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural network adaptive robust control of nonlinear systems in semi-strict feedback form
Automatica (Journal of IFAC)
Adaptive NN control of uncertain nonlinear pure-feedback systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Adaptive NN control for a class of strict-feedback discrete-time nonlinear systems
Automatica (Journal of IFAC)
An ISS-modular approach for adaptive neural control of pure-feedback systems
Automatica (Journal of IFAC)
Direct adaptive NN control of a class of nonlinear systems
IEEE Transactions on Neural Networks
Further results on adaptive control for a class of nonlinear systems using neural networks
IEEE Transactions on Neural Networks
Robust and adaptive backstepping control for nonlinear systems using RBF neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks
IEEE Transactions on Neural Networks
Output feedback control of a class of discrete MIMO nonlinear systems with triangular form inputs
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Neuro-adaptive force/position control with prescribed performance and guaranteed contact maintenance
IEEE Transactions on Neural Networks
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In this brief, a new adaptive neurocontrol algorithm for a single-input-single-output (SISO) strict-feedback nonlinear system is proposed. Most of the previous adaptive neural control algorithms for strict-feedback nonlinear systems were based on the backstepping scheme, which makes the control law and stability analysis very complicated. The main contribution of the proposed method is that it demonstrates that the state-feedback control of the strict-feedback system can be viewed as the output-feedback control problem of the system in the normal form. As a result, the proposed control algorithm is considerably simpler than the previous ones based on backstepping. Depending heavily on the universal approximation property of the neural network (NN), only one NN is employed to approximate the lumped uncertain system nonlinearity. The Lyapunov stability of the NN weights and filtered tracking error is guaranteed in the semiglobal sense.