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首页> 外文期刊>Applied Mathematical Modelling >Multi-objective active control policy design for commensurate and incommensurate fractional order chaotic financial systems
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Multi-objective active control policy design for commensurate and incommensurate fractional order chaotic financial systems

机译：相称和不相称分数阶混沌金融系统的多目标主动控制策略设计

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In this study, an active control policy design is proposed for a fractional order financial system, which considers multiple conflicting objectives. An active control template is used as a nonlinear state feedback mechanism and the controller gains are selected within a multi-objective optimization (MOO) framework to satisfy the conditions of asymptotic stability, which are derived analytically. The MOO obtains a set of solutions on the Pareto optimal front for the multiple conflicting objectives that are considered. We demonstrate that there is a trade-off between the multiple design objectives where better performance for one objective can only be obtained at the cost of degrading the performance for the other objectives. The multi-objective controller design was compared using three different MOO techniques, i.e., non-dominated sorting genetic algorithm-Ⅱ, epsilon variable multi-objective genetic algorithm, and multi-objective evolutionary algorithm with decomposition. The robustness of the same control policy designed with the nominal system settings was also investigated with gradual decrease in the commensurate and incommensurate fractional orders of the financial system.
机译：在这项研究中，提出了一种考虑多个目标冲突的分数阶财务系统的主动控制策略设计。主动控制模板用作非线性状态反馈机制，并在多目标优化（MOO）框架内选择控制器增益以满足渐进稳定性的条件，这些条件是通过分析得出的。 MOO在帕累托最优前沿上针对所考虑的多个冲突目标获得了一组解决方案。我们证明，在多个设计目标之间需要权衡取舍，其中只有以降低其他目标的性能为代价才能获得一个目标的更好性能。利用三种不同的MOO技术对多目标控制器的设计进行了比较，分别是：非支配排序遗传算法Ⅱ，ε变量多目标遗传算法和带分解的多目标进化算法。还研究了用名义系统设置设计的相同控制策略的鲁棒性，并逐渐减少了金融系统相应和不恰当的分数定单。

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