Internal-Model-Principle-Based Fast Adaptive Iterative Learning Trajectory Tracking Control for Autonomous Farming Vehicle Under Alignment Condition and Input Constraint
提出一种新型自适应迭代学习控制策略，用于自主农用车辆在重复性交替直线和大曲率路径下的轨迹跟踪，通过集成指数衰减、内模原理和输入约束辅助变量设计，提升控制系统的动态和静态性能。
A novel adaptive iterative learning control (NAILC) strategy is proposed to enhance static and dynamic control performances for the autonomous farming vehicle tracking repetitive trajectories of alternating parallel straight and large curvature. The method integrates an exponential decay function, an internal model principle (IMP), and an input constraint auxiliary variable design system in an adaptive iterative learning control framework. The introduction of exponential decay improves the convergence rate of the iterative process and ensures the control system’s dynamic performance. The IMP enhances the static performance of the trajectory tracking control system and guarantees the alignment condition setting. The auxiliary variable design system reduces the adverse effects of input constraints on the control system performance during the iterative process. Moreover, the adaptive iterative learning updating law estimates the total time-varying disturbance to improve the robustness of the tracking process against the actual disturbance. Simulation comparisons with existing results verify the effectiveness and advantages of the designed NAILC strategy under the MATLAB/Simulink environment.