A data-driven indirect method for nonlinear optimal control
A data-driven indirect method for nonlinear optimal control
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DOI：
10.1007/s42064-019-0051-3
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发表时间：
2019-12-01
期刊：
影响因子：
6.1
通讯作者：
Hauser, Kris
中科院分区：
文献类型：
--
作者：
Tang, Gao;Hauser, Kris
非线性最优控制问题是具有挑战性的解决，由于当地的最小值，防止收敛和/或最优的流行。本文介绍了最近邻最优控制（NNOC），一个数据驱动的框架，非线性最优控制使用间接的方法。它确定新问题的初始猜测与类似问题的预先计算的解决方案的帮助下，检索使用k-最近的邻居。引入了一种灵敏度分析技术，根据新问题和预先计算的问题的参数变化来线性近似新问题和预先计算的问题之间的解的变化。实验结果表明，NNOC比标准随机重启方法能更快地获得全局最优解，灵敏度分析能进一步将求解时间减少近一半。示例显示在车辆控制和敏捷卫星重新定向的最优控制问题上，证明全局最优值可以在10-100毫秒的顺序内的时间内确定超过99%的可靠性。
Nonlinear optimal control problems are challenging to solve due to the prevalence of local minima that prevent convergence and/or optimality. This paper describes nearest-neighbors optimal control (NNOC), a data-driven framework for nonlinear optimal control using indirect methods. It determines initial guesses for new problems with the help of precomputed solutions to similar problems, retrieved using k-nearest neighbors. A sensitivity analysis technique is introduced to linearly approximate the variation of solutions between new and precomputed problems based on their variation of parameters. Experiments show that NNOC can obtain the global optimal solution orders of magnitude faster than standard random restart methods, and sensitivity analysis can further reduce the solving time almost by half. Examples are shown on optimal control problems in vehicle control and agile satellite reorientation demonstrating that global optima can be determined with more than 99% reliability within time at the order of 10-100 milliseconds.