基于遗传算法优化的磁流变减震结构模糊控制研究

Study on fuzzy control of magneto-rheological structure based on genetic algorithm optimization

基于模糊控制的磁流变减震器能够实现对框架结构的减震,但模糊控制器的设计容易受人主观经验的影响而导致控制效果不够理想。为此,本文提出一种遗传算法双优化模糊控制器设计方法,即运用遗传算法同时优化模糊控制器的模糊规则和隶属函数以改善控制效果。为了验证该方法的有效性,对每层均装有一个磁流变减震器的一个三层框架结构进行了仿真分析。分析结果表明:与未控结构、模糊控制器未优化的结构、模糊控制器只优化模糊规则的结构的位移和加速度反应比较,采用遗传算法双优化模糊控制器选择磁流变减震器控制电流的结构的振动控制效果较好。

Based on the fuzzy control algorithm, the magneto-rheological(MR) damper can be used to mitigate the vibration of building structures, but the design of the fuzzy controller is easily influenced by the subjective experience, which will lead to unsatisfactory control effect. A fuzzy controller design method is proposed based on genetic optimization algorithm, which uses genetic algorithm simultaneously to optimize the fuzzy rules and membership functions of the fuzzy controller to improve the control effect. In order to verify the effectiveness of the proposed method, a simulation analysis is carried out on a three-story building structure with MR damper on each floor. Results show that compared with the displacement and acceleration responses of the uncontrolled structure, the controlled structure using the unoptimized fuzzy controller and the controlled structure using the fuzzy controller with optimized fuzzy rules, the vibration control effect of the structure controlled by the fuzzy controller with simultaneously optimized fuzzy rules and membership functions by genetic algorithm is better.