Design of Multilateral Collaborative Adaptive Learning Controller for Nonlinear Dynamic System with External Disturbance and Backlash-like Hysteresis

For lumped uncertainty caused by hysteresis saturated input, external disturbance and model uncertainty, traditional adaptive control accuracy depends on unilateral parameter learning and nonlinear approximation ability. To improve learning capability and trajectory-tracking performance under the considered uncertainty conditions, a multilateral collaborative adaptive learning mechanism and its corresponding controller are proposed. The initial value allocation of the multilateral uncertainty estimates are realized by means of arithmetic sequence or historical data points, and then the estimates are adjusted online based on the estimation errors of each side. The output result of multilateral weighted fusion is used as the compensation term of lumped uncertainty. An iterative learning framework based on compensation terms, dynamic self-learning approximation errors and the uncertainty estimates of each side is constructed to realize multilateral synchronous cyclic iterative learning, and a saturation transfer function is designed to ensure bounded continuous parameter changes. Controllers based on the linear extended state observer and the nonlinear extended state observer are taken as comparison schemes, the control characteristics of the proposed controller in manipulator systems are studied, and the effectiveness of the controller is verified.