Iterative learning control for optimal path following problems
Iterative learning control for optimal path following problems
复制标题
最优路径跟踪问题的迭代学习控制
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
J. Swevers
中科院分区：
文献类型：
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作者：
P. Janssens;W. V. Loock;G. Pipeleers;Frederik Debrouwere;J. Swevers
在最优路径跟踪问题中，沿给定几何路径的运动沿着根据期望的目标进行优化，同时考虑系统动力学和系统约束。在时间最优路径跟随的情况下，例如，计算在最短时间内沿几何路径沿着移动的系统输入。然而，在实践中，由于模型-设备失配，（i）几何路径没有被精确地遵循，以及（ii）优化的轨迹可能是次优的，或者甚至对于真实设备是不可行的。假设系统重复执行任务，本文提出了一种迭代学习控制方法，以提高路径跟踪性能。所提出的学习算法的实验验证的时间最优路径跟踪问题的XY表。实验结果表明，改进的迭代学习控制方法显著提高了算法的执行时间和精度。
In optimal path following problems the motion along a given geometric path is optimized according to a desired objective while accounting for the system dynamics and system constraints. In the case of time-optimal path following, for example, the system input to move along the geometric path in minimal time is computed. In practice however, due to model-plant mismatch, (i) the geometric path is not followed exactly, and (ii) the optimized trajectory might be suboptimal, or even infeasible for the true plant. Assuming that the system performs the task repeatedly, this paper proposes an iterative learning control approach to improve the path following performance. The proposed learning algorithm is experimentally validated for a time-optimal path following problem on an XY-table. The results show that the developed ILC approach improves both the execution time and the accuracy significantly.