Iterative Learning Control of Minimum Energy Path Following Tasks for Second-Order MIMO Systems: An Indirect Reference Update Framework
针对路径跟踪任务中跟踪时间未指定的问题，提出一种间接参考更新框架，通过离散化方法求解最小化控制能量的运动轮廓，并设计迭代学习控制算法提升精度和鲁棒性，在龙门机器人平台上验证了节能和跟踪性能的优越性。
In a large range of manufacturing tasks, the design objective is characterised as following a given path defined in space. In these applications, the tracking time of any particular position along the path is not specified, so an appropriate motion profile can be chosen among its admissible solutions to improve its tracking performance. This article develops an indirect reference update framework that maximizes accuracy while embedding practical constraints. An optimal path planning problem, incorporating system constraints, is formulated and can be solved using a discretized approach to derive a motion profile that minimizes control energy for a broad spectrum of industrial tasks. To satisfy robustness concerns, an iterative learning control (ILC) algorithm with an indirect reference update framework is designed to improve the accuracy and robustness of path following. It is evaluated on a gantry robot test platform, and the results illustrate superior levels of practical performance in terms of energy reduction and path following accuracy compared with existing approaches.