基于自适应迭代学习的机械手轨迹跟踪控制研究

Research on Trajectory Tracking Control of Manipulator Based on Adaptive Iterative Learning

针对具有初始误差的机械手轨迹跟踪控制问题,设计了一种带初态学习的模糊自适应迭代学习控制策略。控制策略中引入了初态学习控制律,放宽了系统初始状态严格重复的限制,利用Lyapunov函数对系统进行收敛性分析,克服了系统全局Lipschitz连续条件的约束,同时设计了模糊控制器对增益以及自适应律参数进行整定,最后将算法应用到机械手控制中,通过与传统自适应迭代学习控制对比,前者收敛速度和精度明显提高,验证了控制策略的有效性。

A fuzzy adaptive iterative learning control strategy with initial state learning was designed to solve the trajectory tracking control problem of manipulator with initial state error. The initial state learning control law was introduced into the control strategy,which relaxes the restriction of strict initial state of the initial state of the system. The Lyapunov function was used to analyze the convergence of the system,and overcome the constraints of the global Lipschitz continuity condition of the system. At the same time,a fuzzy controller was designed to adjust the gain and adaptive law parameters. Finally,the algorithm was applied to the manipulator control. Compared with the traditional adaptive iterative learning control,the convergence speed and accuracy of the former are obviously improved,which verifies the effectiveness of the control strategy.