Adaptive control using neural networks
Neural networks for control
A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems
Automatica (Journal of IFAC)
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Approximation-based control of nonlinear MIMO time-delay systems
Automatica (Journal of IFAC)
Adaptive output feedback tracking control of robot manipulators using position measurements only
Expert Systems with Applications: An International Journal
Brief paper: Localized adaptive bounds for approximation-based backstepping
Automatica (Journal of IFAC)
Expert Systems with Applications: An International Journal
Brief paper: Neural network compensation control for mechanical systems with disturbances
Automatica (Journal of IFAC)
Motion control with deadzone estimation and compensation using GRNN for TWUSM drive system
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Wavelet differential neural network observer
IEEE Transactions on Neural Networks
ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
Observer-based adaptive fuzzy-neural control for unknown nonlineardynamical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Reinforcement learning-based output feedback control of nonlinear systems with input constraints
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust backstepping control of nonlinear systems using neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Robust adaptive control of nonlinear systems with unknown time delays
Automatica (Journal of IFAC)
Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems
IEEE Transactions on Neural Networks
Identification of Nonlinear Systems With Unknown Time Delay Based on Time-Delay Neural Networks
IEEE Transactions on Neural Networks
Robust Adaptive Observer Design for Uncertain Systems With Bounded Disturbances
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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This paper describes a neural network state observer-based adaptive saturation compensation control for a class of time-varying delayed nonlinear systems with input constraints. An advantage of the presented study lies in that the state estimation problem for a class of uncertain systems with time-varying state delays and input saturation nonlinearities is handled by using the NNs learning process strategy, novel type Lyapunov-Krasovskii functional and the adaptive memoryless neural network observer. Furthermore, by utilizing the property of the function tanh^2(@q/@e)/@q, NNs compensation technique and backstepping method, an adaptive output feedback controller is constructed which not only efficiently avoids the problem of controller singularity and input saturation, but also can achieve the output tracking. And the proposed approach is obtained free of any restrictive assumptions on the delayed states and Lispchitz condition for the unknown nonlinear functions. The semiglobal uniform ultimate boundedness of all signals of the closed-loop systems and the convergence of tracking error to a small neighborhood are all rigorously proven based on the NN-basis function property, Lyapunov method and sliding model theory. Finally, two examples are simulated to confirm the effectiveness and applicability of the proposed approach.