Neural adaptive chaotic control with constrained input using state and output feedback |
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Authors: | Gao Shi-Gen Dong Hai-Rong Sun Xu-Bin Ning Bin |
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Affiliation: | a State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;b School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China |
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Abstract: | This paper presents neural adaptive control methods for a class of chaotic nonlinear systems in the presence of constrained input and unknown dynamics.To attenuate the influence of constrained input caused by actuator saturation,an effective auxiliary system is constructed to prevent the stability of closed loop system from being destroyed.Radial basis function neural networks(RBF-NNs)are used in the online learning of the unknown dynamics,which do not require an off-line training phase.Both state and output feedback control laws are developed.In the output feedback case,high-order sliding mode(HOSM)observer is utilized to estimate the unmeasurable system states.Simulation results are presented to verify the effectiveness of proposed schemes. |
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Keywords: | chaotic control neural adaptive control constrained input |
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