基于多目标克隆选择算法的自适应模糊控制器设计

Design of Adaptive Fuzzy Controllers of Brushless DC Motor Based on Multi-objective Clonal Selection Algorithm

针对无刷直流电机自适应模糊控制器的优化问题,提出了一种利用基于Pareto最优的多目标克隆选择算法来优化控制系统中的模糊控制规则库的方法。首先确定模糊隶属度函数,然后以电机转速的超调量、上升时间和过渡时间作为优化的目标函数,利用多目标克隆选择算法优化专家经验规则库,使各项指标达到最优。无刷直流电机速度控制系统采用电流环和转速环双闭环控制系统,其中转速环采用多目标克隆选择算法优化的自适应模糊控制器进行控制,而电流环则采用传统的PI控制方法。仿真实验结果表明所设计的无刷直流电机自适应模糊控制系统,响应快、无超调,并且具有较强的自适应性和鲁棒性。

According to optimization problems on self-adaptive fuzzy controller of brushless DC motor, a de- sign method of determining the fuzzy control rule base based on the Pareto optimal multi-objective clonal se- lection algorithm was proposed. Firstly fixed the membership function, and then with the overshoot amount, rise time and transition time of motor speed as the optimization objective function and adjusted and optimized the membership functions by using multi-objective clonal selection algorithms further. The current and speed double close-loop control was adopted, in which the fuzzy adaptive controller based on genetic optimization was applied in the speed loop. The simulation results show that the designed brushless DC motor self-adap- tive fuzzy control system has fast dynamic response characteristic, non-overshoot, good adaptability and ro- bustness.