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Indirect iterative learning control for robot manipulator with non-Gaussian disturbances

Indirect iterative learning control for robot manipulator with non-Gaussian disturbances

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In this study, a novel indirect iterative learning control (ILC) strategy is presented for a robotic manipulator that performs repeat operation and is also subjected to non-Gaussian disturbances. The performance index about the entropy of tracking error and the related optimisation method are used to update the local parameters of controller between any two adjacent batches. Moreover, the entropy is employed as it is a unified probabilistic measure of uncertainty quantification regardless whether the random disturbances are Gaussian distribution or not. Thus, an innovative performance index about tracking error entropy that represents the relationship between the entropy of error and controller gains is proposed in order to obtain the controller which can drive the uncertainty of output error as small as possible with the increase of the batch number. Then the non-linear stochastic optimal method is presented so as to obtain updated gains for the next batch. Stability of closed-loop system is analysed. Finally, a comparison between a classic ILC and the proposed approach is given. Moreover, the effectiveness and feasibility of the proposed control schemes is verified by some simulation results of robotic trajectory tracking.

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