Multi-criteria optimization in control of switched systems

P. Drąg, D. Zelazny
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Abstract

In the paper we present a new method for solving optimal control problems of a class of hybrid systems. We describe the new effective algorithm based on memetic algorithm (MA) for optimal control of switched systems. We concentrate on a class of problems in which a pre-specified sequence of active subsystems is known. Our aim is to find both the optimal switching instants and the optimal continuous inputs. The new approach, which we propose, decomposes the cost functional of the basic optimal control problem in Bolza form in two terms. The first term depends explicitly on a value of state variables at the final time. The second term depends on state and control trajectories. In order to solve those two tasks we used MA as the multi-objective optimization algorithm. In this paper we considered a fundamental bi-criteria case with two mentioned before functions: the value of state variables at the final time and the state and control trajectories. In order to find an approximation of Pareto frontier, we proposed new effective method based on genetic algorithm (GA) and local search (LS). Problems properties were taken into consideration in the design of our new approach of solving it. They were used to construct new algorithm inspired by the LS NSGA-II, which performed rather well in multi-criteria scheduling problem. Since simple genetic algorithms are efficient heuristics in searching for optimal solutions, but lack the accuracy of some more computational complex algorithms, a hybrid algorithm was constructed. It uses fast non-dominated sorting, in order to evaluate child population and allocate solutions to corresponding Pareto frontiers. I addition a local search method was used, in order to find more differentiated and better solutions. Clustering solutions from Pareto frontiers also improved diversity of solutions in child population. This approach can be used as a start point for searching for algorithm for solving optimal control problems of switched systems without pre-specified sequence of active subsystems. The performance of the algorithm is illustrated by the examples: (a) a hybrid optimal control problem with nonlinear dynamics and (b) a switched linear quadratic optimal control problem with long switching time intervals.
切换系统控制中的多准则优化
本文给出了求解一类混合系统最优控制问题的一种新方法。提出了一种基于模因算法的切换系统最优控制新算法。我们集中研究一类问题,其中预先指定的活动子系统序列是已知的。我们的目标是找到最优的切换时刻和最优的连续输入。我们提出的新方法将基本最优控制问题的代价函数分解为Bolza形式的两项。第一项显式地取决于最后时刻状态变量的值。第二项取决于状态和控制轨迹。为了解决这两个问题,我们采用了多目标优化算法。在本文中,我们考虑了一个基本的双准则情况,其中包含了之前提到的两个函数:状态变量在最终时刻的值以及状态和控制轨迹。为了寻找Pareto边界的近似,提出了一种基于遗传算法和局部搜索的Pareto边界逼近方法。在设计解决问题的新方法时,考虑了问题的性质。在LS NSGA-II的启发下,构建了一种新的算法,该算法在多准则调度问题中表现良好。由于简单遗传算法在寻找最优解方面是一种高效的启发式算法,但缺乏一些计算量更大的复杂算法的准确性,因此构造了一种混合算法。它使用快速非支配排序,以评估儿童群体和分配解决方案到相应的帕累托边界。另外,采用局部搜索的方法,寻找更有差别化、更好的解。来自Pareto边界的聚类解决方案也提高了儿童群体解决方案的多样性。该方法可作为求解无预定活动子系统序列的切换系统最优控制问题的算法的起点。实例说明了该算法的性能:(a)具有非线性动力学的混合最优控制问题和(b)具有长切换时间间隔的切换线性二次最优控制问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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