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A Genetic Algorithm based Fractional Fuzzy PID Controller for Integer and Fractional order Systems

机译:基于遗传算法的整数和分数阶系统分数阶模糊PID控制器

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This work aims at designing a fractional Proportional-Integral-Derivative controller wherein we hybridize a genetic algorithm based fractional Proportional-Integral-Derivative controller with a fuzzy logic Proportional-Integral-Derivative controller. We attempt at optimizing the fractional order Proportional-Integral-Derivative controller parameters by incorporating a Genetic Algorithm based mechanism. Thereafter, the optimized genetic algorithm based fractional Proportional-Integral-Derivative control is further fine tuned and hybridized to a fuzzy Proportional-Integral-Derivative control. Here, fuzzy logic based inference mechanism is used to tackle system uncertainties and use of rule firing strengths produces an adaptive control. Genetic Algorithm has been used to generate the most optimal controller by a natural selection of the fittest. Amalgamating Genetic Algorithm and fuzzy control approaches on fractional order systems produces a highly efficient and noise tolerant control regime. We give simulation results and compare our hybrid approach against conventional and fractional Proportional-Integral-Derivative approaches on various integer and fractional order systems (with dead time) to elucidate its superiority and effectiveness.
机译:这项工作旨在设计分数比例-积分-微分控制器,其中,我们将基于遗传算法的分数比例-积分-微分控制器与模糊逻辑比例-积分-微分控制器进行混合。我们试图通过结合基于遗传算法的机制来优化分数阶比例-积分-微分控制器参数。此后,进一步优化基于遗传算法的分数比例-积分-微分控制,并将其与模糊比例-积分-微分控制混合。此处,基于模糊逻辑的推理机制用于解决系统不确定性,使用规则激发强度可产生自适应控制。通过优胜劣汰的自然选择,遗传算法已用于生成最佳控制器。分数阶系统上的混合遗传算法和模糊控制方法产生了高效且耐噪声的控制机制。我们给出了仿真结果,并在各种整数和分数阶系统(具有停滞时间)下将混合方法与常规和分数比例-积分-微分方法进行了比较,以阐明其优越性和有效性。

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