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Optimization of Fractional and Integer Order PID Parameters using Big Bang Big Crunch and Genetic Algorithms for a MAGLEV System

机译:使用大爆炸大谐仓和遗传算法的分数和整数PID参数优化Maglev系统的遗传算法

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This paper presents a study on optimized control for a magnetically levitated (MAGLEV) suspension system. Unstable magnetically levitated system is modelled and integer order PID (IOPID) and fractional order PID (FOPID) controller parameters are evaluated by using both Genetic Algorithm (GA) and Big Bang Big Crunch (BBBC) algorithm. Comparison between BBBC and GA based controllers are done. Responses for variable reference inputs are obtained. Results show that the performance of the BBBC based FOPID controller is better than GA optimized FOPID controller.
机译:本文介绍了对磁悬浮(Maglev)悬架系统的优化控制的研究。通过使用遗传算法(GA)和BBBC)算法(BBBC)算法,评估不稳定的磁悬浮系统和整数PID(IOPID)和分数级PID(FOPID)控制器参数。完成了BBBC和GA基于GA控制器之间的比较。获得了可变参考输入的响应。结果表明,基于BBBC的FoPID控制器的性能优于GA优化的FoPID控制器。

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