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首页> 外文期刊>Expert systems with applications >Reliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA)
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Reliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA)

机译：使用多目标均匀多样性遗传算法（MUGA）的线性状态反馈控制器的基于可靠性的鲁棒Pareto设计

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摘要

In this paper, fuzzy threshold values, instead of crisp threshold values, have been used for optimal reliability-based multi-objective Pareto design of robust state feedback controllers for a single inverted pendulum having parameters with probabilistic uncertainties. The objective functions that have been considered are, namely, the normalized summation of rising time and overshoot of cart (S_(RO)-C) and the normalized summation of rising time and overshoot of pendulum (S_(RO)-P) in the deterministic approach. Accordingly, the probabilities of failure of those objective functions are also considered in the reliability-based design optimization (RBDO) approach. A new multi-objective uniform-diversity genetic algorithm (MUGA) is presented and used for Pareto optimum design of linear state feedback controllers for single inverted pendulum problem. In this way, Pareto front of optimum controllers is first obtained for the nominal deterministic single inverted pendulum using the conflicting objective functions in time domain. Such Pareto front is then obtained for single inverted pendulum having probabilistic uncertainties in its parameters using the statistical moments of those objective functions through a Monte Carlo simulation (MCS) approach. It is shown that multi-objective reliability-based Pareto optimization of the robust state feedback controllers using MUGA with fuzzy threshold values includes those that may be obtained by various crisp threshold values of probability of failures and, thus, remove the difficulty of selecting suitable crisp values. Besides, the multi-objective Pareto optimization of such robust feedback controllers using MUGA unveils some very important and informative trade-offs among those objective functions. Consequently, some optimum robust state feedback controllers can be compro-mismgly chosen from the Pareto frontiers.
机译：在本文中，模糊阈值而不是清晰阈值已用于具有参数不确定性的单个倒立摆的基于最优可靠性的基于鲁棒状态反馈控制器的多目标Pareto设计。所考虑的目标函数是，上升时间和小车的超调量（S_（RO）-C）的标准化总和以及上升时间和摆的超调量（S_（RO）-P）的标准化总和。确定性方法。因此，在基于可靠性的设计优化（RBDO）方法中还考虑了那些目标函数失效的可能性。提出了一种新的多目标均匀多样性遗传算法，用于单倒立摆问题的线性状态反馈控制器的帕累托最优设计。以此方式，首先使用时域中的冲突目标函数为标称确定性单个倒立摆获得最优控制器的Pareto前沿。然后，通过蒙特卡罗模拟（MCS）方法，使用那些目标函数的统计矩，针对参数中具有概率不确定性的单个倒立摆获得这种帕累托前沿。结果表明，使用具有模糊阈值的MUGA的鲁棒状态反馈控制器的基于多目标可靠性的Pareto优化包括可以通过各种故障概率的明晰阈值获得的那些，从而消除了选择合适的明晰度的难度价值观。此外，使用MUGA对这种鲁棒的反馈控制器进行多目标Pareto优化，揭示了这些目标函数之间一些非常重要且信息丰富的折衷方案。因此，可以从帕累托边界中适当地选择一些最佳的鲁棒状态反馈控制器。

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  • 来源
    《Expert systems with applications》 |2010年第1期|401-413|共13页
  • 作者

    A. Jamali; A. Hajiloo; N. Nariman-zadeh;

  • 作者单位

    Department of Mechanical Engineering, Faculty of Engineering, The University of Guilan, P.O. Box 3756, Rasht, Iran;

    Department of Mechanical Engineering, Faculty of Engineering, The University of Guilan, P.O. Box 3756, Rasht, Iran;

    Department of Mechanical Engineering, Faculty of Engineering, The University of Guilan, P.O. Box 3756, Rasht, Iran Intelligent-Based Experimental Mechanics Center of Excellence, School of Mechanical Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran;

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  • 正文语种 eng
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  • 关键词

    pareto; robust control; multi-objective optimization; genetic algorithms; monte Carlo simulation;

    机译：帕雷托鲁棒的控制;多目标优化;遗传算法;蒙特卡罗模拟;

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