基于遗传算法优化的LQR主动磁悬浮轴承控制

主动磁悬浮轴承(AMB)是利用电磁线圈将电能转化成电磁能为转轴提供支撑的机械装置。在当代工业制造领域,相较于传统轴承,主动磁悬浮轴承因为拥有无机械摩擦损耗、使用转速极高、功耗小、噪声低等优点,经常用于对转速、精度、使用环境有特殊要求的场合。针对此提出一种基于遗传算法优化的LQR控制方式,能够利用最优控制的快速响应、超调量小的优点实现轴承的迅速悬浮定位,同时使用遗传算法针对最优控制的Q和R参数进行优化。遗传算法通过模拟生物进化的过程搜索最优解的计算模型,根据系统输出量的误差设定遗传算法的适应度,得到目标函数的近似最优解,最后将优化后的参数代入模型,在MATLAB平台实现仿真运行。

LQR Active Magnetic Bearing Control Based on Genetic Algorithm Optimization

Active magnetic bearing (AMB) is a mechanical device that uses electromagnetic coils to convert electrical energy into magnetic energy to support the rotating shaft. In the field of contemporary industrial manufacturing, compared with traditional bearings, active magnetic bearings are often used in occasions with special requirements for speed, accuracy and operating environment due to their advantages such as no mechanical friction loss, extremely high operating speed, low power consumption and low noise. An optimized LQR control method based on genetic algorithm (GA) was adopted, utilizing the advantages of quick response and small overshoot of the optimal control to realize rapid suspension and stable position of the bearing. Meanwhile, genetic algorithm was used to optimize the Q matrix and R matrix of the optimal controller. In the genetic algorithm, the optimal solution was searched by simulating the process of biological evolution. The fitness of the genetic algorithm was set according to the deviation of the system to obtain the approximate optimal solution of Q and R matrix. At last, the simulation was realized on MATLAB platform.