Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Control of Robot Manipulators
Journal of Intelligent and Robotic Systems
Journal of Intelligent and Robotic Systems
Design of Adaptive Robot Control System Using Recurrent Neural Network
Journal of Intelligent and Robotic Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Robust backpropagation training algorithm for multilayered neural tracking controller
IEEE Transactions on Neural Networks
New results on recurrent network training: unifying the algorithms and accelerating convergence
IEEE Transactions on Neural Networks
Neural network-based adaptive controller design of robotic manipulators with an observer
IEEE Transactions on Neural Networks
Neural net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
International Journal of Advanced Intelligence Paradigms
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In this paper, a recurrent neural network (RNN) control scheme is proposed for a biped robot trajectory tracking system. An adaptive online training algorithm is optimized to improve the transient response of the network via so-called conic sector theorem. Furthermore, L 2-stability of weight estimation error of RNN is guaranteed such that the robustness of the controller is ensured in the presence of uncertainties. In consideration of practical applications, the algorithm is developed in the discrete-time domain. Simulations for a seven-link robot model are presented to justify the advantage of the proposed approach. We give comparisons between the standard PD control and the proposed RNN compensation method.