Stochastic nonlinear stabilization—I: a backstepping design
Systems & Control Letters
Nonlinear and Adaptive Control Design
Nonlinear and Adaptive Control Design
Optimal Sampled-Data Control Systems
Optimal Sampled-Data Control Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Stable Adaptive Neural Network Control
Stable Adaptive Neural Network Control
Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximation Techniques
Brief Paper: Design and performance analysis of a direct adaptive controller for nonlinear systems
Automatica (Journal of IFAC)
Stable adaptive neuro-control design via Lyapunov function derivative estimation
Automatica (Journal of IFAC)
Lyapunov-based continuous-time nonlinear controller redesign for sampled-data implementation
Automatica (Journal of IFAC)
Stable neural-network-based adaptive control for sampled-data nonlinear systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
High-order neural network structures for identification of dynamical systems
IEEE Transactions on Neural Networks
An Adaptive NN Controller with Second Order SMC-Based NN Weight Update Law for Asymptotic Tracking
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Adaptive control based on IF-THEN rules for grasping force regulation with unknown contact mechanism
Robotics and Computer-Integrated Manufacturing
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In this paper, for a class of single-input-single-output (SISO) uncertain nonlinear systems, adaptive neural tracking controllers designed for digital computer implementation are proposed. The overall scheme can be considered as a sampled-data adaptive neural control system. As an intermediate result, it is proven that, for a sufficiently small sampling period, the emulated adaptive neural controller i.e., the discrete implementation of the continuous-time adaptive neural network controller ensures semiglobal uniformly ultimate boundedness of the closed-loop system. Then, based on the exact discrete-time model, a controller redesign is proposed that performs efficiently for sampling periods for which the emulation controller fails. The redesigned controller consists of two terms: the emulated control law and an extra robustness term designed to increase the order of the perturbation (with respect to the sampling period) in the Lyapunov difference. In all cases, high-order neural networks are employed to approximate the unknown nonlinearities. Using Lyapunov techniques, it is proven that, for a sufficiently small sampling period, the proposed redesigned controller ensures the (semiglobal) boundedness of all the signals in the closed-loop while the output of the system converges to a small neighborhood of the desired trajectory. Simulation results illustrate the superiority of the proposed scheme with respect to the emulation controller and verify the theoretical analysis.