Trajectory Desensitization in Optimal Control Problems
Trajectory Desensitization in Optimal Control Problems
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DOI：
10.1109/cdc.2018.8619577
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Venkata Ramana Makkapati;Mehregan Dor;P. Tsiotras
中科院分区：
文献类型：
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作者：
Venkata Ramana Makkapati;Mehregan Dor;P. Tsiotras
研究了所谓的灵敏度函数在开发脱敏最优控制方案中的功效。灵敏度函数提供有关在给定时刻沿着轨迹的参数变化下状态的一阶变化的信息。事实证明，灵敏度函数可用于有效地对最优轨迹或沿最优轨迹的特定时刻的状态（例如，最终状态）进行脱敏。选择 Zermelo 的路径优化问题来检验该理论。进行了蒙特卡罗模拟，验证了关键思想。确定了所提出方法的局限性，并讨论了未来工作的可能性。
The efficacy of the so-called sensitivity function in developing desensitized optimal control schemes is studied. A sensitivity function provides information about the first order variation of the state under parameter variations at a given time instant along a trajectory. It is demonstrated that the sensitivity function can be employed to effectively desensitize either an optimal trajectory or the state at a particular time instant (for example, the final state) along the optimal trajectory. Zermelo's path optimization problem is chosen to test the theory. Monte-Carlo simulations are carried out, validating the key idea. The limitations of the proposed approach are identified and the possibilities for future work are discussed.