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Neural network-based robust H∞ decentralised control strategy with new learning algorithm for robot manipulators

In this paper, a new robust H∞ intelligent decentralised control (RHIDC) strategy is proposed for the trajectory tracking problem of robot manipulators. The proposed control system is comprised of a computed torque controller and neural robust controller with new learning algorithm. Based on Lyapunov stability theorem and inequality technology, it is shown that the proposed controller can guarantee stability of closed-loop error systems and satisfactory tracking performances. The proposed approach shows that computed torque control method is also valid for controlling uncertain robotic manipulators as long as compensative controller is appropriately designed.