Constrained Online Optimization Using Evolutionary Operation: A Case Study About Energy‐Optimal Robot Control
Constrained Online Optimization Using Evolutionary Operation: A Case Study About Energy‐Optimal Robot Control
复制标题
使用进化操作的约束在线优化：关于能量最优机器人控制的案例研究
DOI：
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发表时间：
2015
影响因子：
2.3
通讯作者：
B. Ketelaere
中科院分区：
文献类型：
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作者：
K. Rutten;J. Baerdemaeker;J. Stoev;M. Witters;B. Ketelaere
在常规生产过程中优化全规模流程是一项在实践中经常遇到的挑战，需要专门的方法，只引入小扰动，以便生产不需要中断。基于一个案例研究，我们讨论了进化操作（EVOP）派生方法的潜力。该案例研究涉及一个羽毛球机器人，该机器人必须在固定的时间间隔内执行点对点运动，基于两种操作模式：时间最优运动，确保最大精度但最高能耗，以及能量最优运动，降低能耗，但作为权衡也降低了精度。当前的标准运行模式是能量最优模式，该模式是根据模拟的离线优化构建的。实施了在线EVOP最陡上升优化，以通过微调所实施的能量优化模式来进一步降低能量消耗。该问题的约束性质，即能量需要在时间约束下最小化，使用Derringer期望函数将其转换为无约束单目标优化。做出了两个重要贡献：（i）能量最优运动的在线优化在保持精度恒定的同时降低了4.7%的能耗;（ii）在期望函数中实施的更严格的时间约束导致了具有最大精度的操作模式，并且比当前时间最优运动的能耗低51.7%。版权所有© 2014约翰威利父子有限公司.
Optimization of full‐scale processes during regular production is a challenge that is often encountered in practice, requiring specialized approaches that only introduce small perturbations so that production does not need to be interrupted. Based on a case study, we discuss the potential of Evolutionary Operation (EVOP) derived methods. The case study relates to a badminton robot that has to perform point‐to‐point motions during a fixed time interval, based on two operation modes: time‐optimal motion, which ensures maximum precision but highest energy consumption, and energy‐optimal motion, which decreases the energy consumption, but as a trade‐off also lowers the precision. The current standard mode of operation is the energy‐optimal mode that is constructed from off‐line optimization on simulations. An online EVOP steepest ascent optimization to further reduce the energy consumption by fine‐tuning the implemented energy‐optimal mode was implemented. The constrained nature of the problem, where energy needs to be minimized subject to a time constraint, was transformed to an unconstrained single‐objective optimization using Derringer desirability functions. Two important contributions were made: (i) the online optimization of the energy‐optimal motion lowered the energy consumption by 4.7% while keeping the precision constant and (ii) the more stringent time‐constraints implemented in desirability functions lead to an operation mode with maximum precision and 51.7% less energy consumption than the current time‐optimal motion. Copyright © 2014 John Wiley & Sons, Ltd.