机械臂带角度修正的开闭环迭代学习轨迹跟踪控制

在迭代学习控制研究中, 通常的一个假设是: 系统每次迭代初态与期望初态一致或迭代初态固定. 针对迭代学习控制律在迭代初态的限制下难以应用到机械臂轨迹跟踪控制中的问题, 本文对机械臂系统模型降阶变换, 将其转化为低阶系统. 对于变换设计后的机械臂系统模型, 提出一种带有角度修正的开闭环迭代学习控制算法, 该算法利用误差信号及相邻两次误差的偏差信号对系统控制律进行逐次修正, 与常规P型算法相比, 充分利用了系统已存的和当前的有效信息, 与常规PD型算法相比, 避免了由于微分作用而带来的不稳定影响. 同时, 用输出向量的角度关系作为评估控制输入好坏的标准对所设计的迭代学习律的变化趋势进行“奖-惩”, 从而实现了良好的跟踪效果并具有较快的收敛速度. 本文还针对机械臂系统存在关节转角限位的情况对控制算法进行改进, 以使机械臂在实际运作中真正实时地完成指定工作任务. 仿真结果表明了所提控制策略的有效性.

Open-closed Loop ILC Corrected with Angle Relationship of Output Vectors for Tracking Control of Manipulator

In the research of iterative learning control (ILC), it is usually assumed that the initial states are consistent with the desired states or the initial states are fixed per iteration. By considering the problem that ILC law is difficult to apply to the tracking control for the manipulator under the restriction of initial states, we change the dynamic model of the manipulator system into a lower-order system by reduced-order transformations. For the transformed manipulator system, an open-closed loop ILC algorithm with angle correction term is proposed, which uses the error signal and the deviation of two adjacent error signals to adjust itself. Compared with traditional P-type algorithm, this algorithm makes better use of the saved and current information; while compared with PD-type algorithm, it overcomes the instability caused by the derivative action. Meanwhile, the angle relationship of output vectors is used as a standard to estimate the quality of the control inputs, ``awarding or punishing' the changing trend of the algorithm. So, a fast convergence speed and excellent tracking effect are both realized. Improved strategies are proposed for the above algorithm when the limitation of each joint rotating angle is considered. Finally, the simulation results verify the effectiveness of the control scheme.