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Iterative Learning Control for Minimum Time Path Following

机译：最小时间路径跟随的迭代学习控制

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摘要

The technique of iterative learning control (ILC) is frequently applied to improve the tracking performance of those systems operating repetitively. This paper extends the ILC task description to handle path following problems by relaxing assumptions on fixed motion profiles and trial length. The removal of these design constraints yields significant control design flexibilities to choose an admissible solution of the problem so that an extra performance index is optimized. A two stage design framework is considered to find the minimum path following time, and guarantee a feasible solution of the path following problem under system constraints using ILC. The solutions of the two stages are provided by a norm optimal ILC update and the bisection method with implementation guidelines, which are combined to form a comprehensive implementation algorithm. The effectiveness of the proposed algorithm is evaluated using a case study on a gantry robot model.
机译：迭代学习控制（ILC）技术经常用于改善那些重复运行的系统的跟踪性能。本文通过放松对固定运动曲线和试验长度的假设，将ILC任务描述扩展为处理路径跟踪问题。消除这些设计约束后，就可以在控制设计方面具有很大的灵活性，从而可以选择问题的可接受解决方案，从而优化了额外的性能指标。考虑使用两阶段设计框架来找到最小路径跟随时间，并使用ILC保证系统约束下路径跟随问题的可行解决方案。这两个阶段的解决方案由规范的最佳ILC更新和带有实施准则的二等分方法提供，两者结合起来形成一个综合的实施算法。该算法的有效性通过龙门机器人模型的案例研究进行了评估。

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