Control of Robot Manipulators
Adaptive Feedback Linearization Using Efficient Neural Networks
Journal of Intelligent and Robotic Systems
Adaptive Control of Mechanical Systems Using Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adaptive control for uncertain nonlinear systems based on multiple neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Adaptive neural control of uncertain MIMO nonlinear systems
IEEE Transactions on Neural Networks
Robust and adaptive backstepping control for nonlinear systems using RBF neural networks
IEEE Transactions on Neural Networks
A stable neural network-based observer with application to flexible-joint manipulators
IEEE Transactions on Neural Networks
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The observer-based tracking control problem for a class of nonlinear affine systems using neural networks is proposed in this paper. The controller is based on feedback linearization where the observer system and control signal are directly estimated by a nonlinear in parameter neural networks (NLPNN). A Hebbian-like algorithm with e-modification is used to update the weights of the network. The uniformly ultimately boundedness of the tracking error and all signals in the overall closed-loop system is proved using Lyapunov's direct method. To evaluate the performance of the proposed observer-based controller, a set of simulations is performed on a nonlinear cart-pole system. Simulation results show the effectiveness of the proposed control methodology.