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Study of an Adaptive Iterative Learning Control Based on Extended State Observer in Path Tracking of Double Joint Robot

In this paper, an adaptive iterative learning control algorithm is designed based on extended state observer. Traditional robotic path tracking control has low control precision and poor control performance. A basic structure of the double joint manipulator is used for study. The system model is established, and a manipulator trajectory tracking control scheme is proposed. The specific algorithm principle and implementation method are given. The proposed algorithm consists of variable gain PD control, adaptive control, and extended state observer. The PD control can change the control gain adaptively because of the adaptive feedback loop to ensure the system stability. The extended state observer regards both the internal interference and the external random disturbance of the system. The state is used to compensate for the uncertainty of both. Experiment results show that the proposed algorithm improves the range and performance of the trajectory tracking error while improving the system convergence, and improves the control performance of the system.