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Neural network robust \(H_\infty\) tracking control strategy for robot manipulators. (English) Zbl 1193.93094

Summary: A novel neural-network-based robust \(H_\infty \) control (NNRHC) strategy is proposed for the trajectory following problem of robot manipulators. The proposed system is comprised of a computed torque controller, a variable structure slide (VSS) controller and a neural network robust controller. Based on Lyapunov stability theorem, it is shown that the proposed controller can guarantee \(H_\infty \) tracking performance of robotic system, 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 proposed approach indicates that computed torque control method is also valid for controlling robot manipulators with uncertainties as long as a compensative controller is appropriately designed. Both simulation and experimental results show the superior control performance of the proposed neural control method.

MSC:

93B36 \(H^\infty\)-control
93C85 Automated systems (robots, etc.) in control theory
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