Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neuro-Control Systems: Theory and Applications
Neuro-Control Systems: Theory and Applications
Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximation Techniques
Implementable adaptive backstepping neural control of uncertain strict-feedback nonlinear systems
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
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)
Stable adaptive neuro-control design via Lyapunov function derivative estimation
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
An ISS-modular approach for adaptive neural control of pure-feedback systems
Automatica (Journal of IFAC)
A variable structure MRAC with expected transient and steady-state performance
Automatica (Journal of IFAC)
Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems
IEEE Transactions on Neural Networks
A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Neuro-adaptive force/position control with prescribed performance and guaranteed contact maintenance
IEEE Transactions on Neural Networks
Model-free robot joint position regulation and tracking with prescribed performance guarantees
Robotics and Autonomous Systems
Adaptive control for nonlinear systems with time-varying control gain
Journal of Control Science and Engineering - Special issue on Adaptive Control Theory and Applications
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Two robust adaptive control schemes for single-input single-output (SISO) strict feedback nonlinear systems possessing unknown nonlinearities, capable of guaranteeing prescribed performance bounds are presented in this paper. The first assumes knowledge of only the signs of the virtual control coefficients, while in the second we relax this assumption by incorporating Nussbaum-type gains, decoupled backstepping and non-integral-type Lyapunov functions. By prescribed performance bounds we mean that the tracking error should converge to an arbitrarily predefined small residual set, with convergence rate no less than a prespecified value, exhibiting a maximum overshoot less than a sufficiently small prespecified constant. A novel output error transformation is introduced to transform the original ''constrained'' (in the sense of the output error restrictions) system into an equivalent ''unconstrained''one. It is proven that the stabilization of the ''unconstrained'' system is sufficient to solve the problem. Both controllers are smooth and successfully overcome the loss of controllability issue. The fact that we are only concerned with the stabilization of the ''unconstrained'' system, severely reduces the complexity of selecting both the control parameters and the regressors in the neural approximators. Simulation studies clarify and verify the approach.