Neural network based repetitive learning control of robot manipulators

Control of robot manipulators performing periodic tasks is considered in this work. The control problem is complicated by presence of uncertainties in the robot manipulator's dynamic model. To address this restriction, a model free repetitive learning controller design is aimed. To reduce the heavy control effort, a neural network based compensation term is fused with the repetitive learning controller. The convergence of the tracking error to the origin is ensured via Lyapunov based techniques. Numerical simulations and experiments are performed to demonstrate the viability of the proposed controller.

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