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Neural network-based H tracking control for robotic systems

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1 Author(s)
Y. -C. Chang ; Kung-Shan Inst. of Technol., Tainan Hsien, Taiwan

An adaptive H tracking control design is proposed for robotic systems under plant uncertainties and external disturbances. Three important control design techniques, i.e. nonlinear H tracking theory, variable structure control algorithm and neural network control design, are combined to construct a hybrid adaptive-robust tracking control scheme which ensures that the joint positions track the desired reference signals. It is shown that an H tracking control is achieved in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance on the tracking error can be attenuated to any pre-assigned level. The solution of H control performance relies only on an algebraic Riccati-like matrix equation. A simple design algorithm is proposed such that the proposed adaptive neural network-based H tracking controller can easily be implemented. A simulation example demonstrates the effectiveness of the proposed control algorithm

Published in:

IEE Proceedings - Control Theory and Applications  (Volume:147 ,  Issue: 3 )