Information Sciences: an International Journal
Optimal tracking control for a class of nonlinear time-delay systems with actuator saturation
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
Adaptive neural control for a class of large-scale pure-feedback nonlinear systems
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Output feedback control of large-scale nonlinear time-delay systems in lower triangular form
Automatica (Journal of IFAC)
Control of an Industrial PA10-7CE Robot Arm Based on Decentralized Neural Backstepping Approach
Neural Processing Letters
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In this paper, two adaptive neural network (NN) decentralized output feedback control approaches are proposed for a class of uncertain nonlinear large-scale systems with immeasurable states and unknown time delays. Using NNs to approximate the unknown nonlinear functions, an NN state observer is designed to estimate the immeasurable states. By combining the adaptive backstepping technique with decentralized control design principle, an adaptive NN decentralized output feedback control approach is developed. In order to overcome the problem of “explosion of complexity” inherent in the proposed control approach, the dynamic surface control (DSC) technique is introduced into the first adaptive NN decentralized control scheme, and a simplified adaptive NN decentralized output feedback DSC approach is developed. It is proved that the two proposed control approaches can guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded, and the observer errors and the tracking errors converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approaches.