CAJ | 학술논문

为了提高迭代学习控制方法在移动机器人轨迹跟踪问题中的收敛速度，提出了一种带有可变遗忘因子的离散迭代学习控制算法。该算法是在开闭环离散迭代学习控制律基础上，通过可变遗忘因子对上一次的控制量进行调节，并增加了带有可变遗忘因子的初始修正项。通过适当选取学习律中的初始控制输入，带遗忘因子的初始修正项可以避免迭代轨迹的大幅度摆动，从而可以使迭代学习的收敛速度得到显著提高。并利用范数理论对算法的收敛性进行了严格证明，得到了使算法收敛的范数形式的充分条件。最后通过仿真实验验证了所提算法的有效性。
위료제고질대학습공제방법재이동궤기인궤적근종문제중적수렴속도，제출료일충대유가변유망인자적리산질대학습공제산법。해산법시재개폐배리산질대학습공제률기출상，통과가변유망인자대상일차적공제량진행조절，병증가료대유가변유망인자적초시수정항。통과괄당선취학습률중적초시공제수입，대유망인자적초시수정항가이피면질대궤적적대폭도파동，종이가이사질대학습적수렴속도득도현저제고。병이용범수이론대산법적수렴성진행료엄격증명，득도료사산법수렴적범수형식적충분조건。최후통과방진실험험증료소제산법적유효성。
In order to improve the convergence speed of the iterative learning control method in dealing with the trajectory tracking problem of the mobile robot, this paper presents a discrete iterative learning control algorithm with variable forgetting factor. Based on the open-closed-loop iterative learning control, this algorithm adjusted the last control amount by a variable forgetting factor, and added an initial correction term with the variable forgetting factor. By adaptly choosing the initial control input in leaning rule, the initial correction term with the variable forgetting factor could avoid the amplitude swing of the iterative trajectory, thereby greatly improving the convergence speed of the iterative learning. The convergence of the algorithm was proven strictly by the norm theory, and the convergence condition in the norm form was provided. Finally, simulations verified the validity of the proposed algorithm.