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Novel metaheuristic-based tuning of PID controllers for seismic structures and verification of robustness
Journal of Building Engineering ( IF 6.7 ) Pub Date : 2020-07-09 , DOI: 10.1016/j.jobe.2020.101647
Serdar Ulusoy , Sinan Melih Nigdeli , Gebrail Bekdaş

In the present study, an active structural control using metaheuristic tuned Proportional-Integral-Derivative (PID) type controllers is presented. The aim of the study is to propose a feasible active control application considering time delay and a feasible control force. In the optimum control methodology, near-fault directivity pulse was considered for ground motion. Three different metaheuristic algorithms are separately employed in the optimum tuning of PID parameters such as proportional gain, integral time and derivative time. The employed algorithms are Flower Pollination Algorithm, Teaching Learning Based Optimization and Jaya algorithm. The maximum control force limit is considered as a design constraint. The methodology contains the time delay consideration and a process to avoid the stability problem on the trial results during the optimization process. The method is explained in three stages as The Pre-Optimization Stage, The Dynamic Analysis Stage and The Optimization Stage. The optimum PID parameters of different algorithms are very different, but the performance of active control is similar since a similar control signal can be generated by different proportion of controller gains such as proportion, integral and derivative processes. As the conclusion of the study, the amount of control force must be chosen carefully since big control forces may resulted with stability problems if the control system has long delay.



中文翻译:

基于元启发式的新型PID控制器用于地震结构的调整和鲁棒性验证

在本研究中,提出了一种使用元启发式调整比例积分微分(PID)类型控制器的主动结构控制。该研究的目的是提出一种考虑时间延迟和可行控制力的可行主动控制应用。在最佳控制方法中,考虑将近故障方向性脉冲用于地面运动。在PID参数的最佳调整(例如比例增益,积分时间和微分时间)中,分别采用了三种不同的元启发式算法。所采用的算法是花授粉算法,基于教学学习的优化和Jaya算法。最大控制力极限被视为设计约束。该方法包括时间延迟考虑因素和避免在优化过程中试验结果的稳定性问题的过程。该方法分为三个阶段:预优化阶段,动态分析阶段和优化阶段。不同算法的最佳PID参数有很大不同,但由于不同比例的控制器增益(例如比例,积分和微分过程)可以生成相似的控制信号,因此主动控制的性能相似。作为研究的结论,必须谨慎选择控制力的大小,因为如果控制系统具有较长的延迟,则大的控制力可能会导致稳定性问题。动态分析阶段和优化阶段。不同算法的最优PID参数有很大不同,但是由于不同比例的控制器增益(例如比例,积分和微分过程)可以生成相似的控制信号,因此主动控制的性能相似。作为研究的结论,必须谨慎选择控制力的大小,因为如果控制系统具有较长的延迟,则大的控制力可能会导致稳定性问题。动态分析阶段和优化阶段。不同算法的最优PID参数有很大不同,但是由于不同比例的控制器增益(例如比例,积分和微分过程)可以生成相似的控制信号,因此主动控制的性能相似。作为研究的结论,必须谨慎选择控制力的大小,因为如果控制系统具有较长的延迟,则大的控制力可能会导致稳定性问题。

更新日期:2020-07-09
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