基于模糊控制的无刷直流电机速度控制系统设计

发布时间：2018-04-29 01:07

本文选题：无刷直流电机 + 自适应控制　；参考：《河南科技大学》2014年硕士论文

【摘要】：无刷直流电机以其较小体积，较高功率密度，，结构简单，以及更好的速度调节性能等优点，在机电能量转换领域得以广泛应用。由于无刷直流电机的多变量，强耦合，非线性等特点，传统的PID控制方法对于在线寻找适合的PID参数十分困难，很难达到理想的效果。自适应控制算法对参数的分辨识别和电机状态的预估都是相对于线性模型的，而对于非线性的控制对象，很难满足控制的精确性和实时性。模糊控制不要求掌握被控对象的精确模型，但是主观的专家控制规则无法满足不同对象的控制要求。克隆选择算法可以有效的搜索到全局最优解，并避免陷入局部最优解。本文将多种控制策略相互结合，设计了基于多目标克隆选择算法优化的模糊自适应PID控制器。本文简述了无刷直流电机的组成结构和运行原理，对无刷直流电机的数学模型进行了学习，完成了对无刷直流电机的双闭环控制系统的建模。系统的外环转速调节模块利用多目标克隆选择算法优化过的模糊自适应PID控制器的，内环调节器则采用传统的PI控制器。本文提出了利用多目标克隆选择算法优化模糊控制规则，以及基于精英导向机制的模糊控制两种方法。这两种优化方法都可以提高无刷直流电机的控制性能：与常规的控制器相比，系统的响应时间得到了很好的提升，能够很快的达到稳定状态，相对于常规控制器来说具有较高的控制精度。只是多目标克隆选择算法优化模糊自适应PID控制器的控制规则，虽然能够获得控制规则整体上的最优，但无法满足决策者的偏好，而加入精英导向机制策略的控制器则能够可根据决策偏好快速有效地定向搜索Pareto最优解。在Matlab2012/Simulink中搭建了包括电机本体主回路模块、逻辑换相模块、速度环模块以及电流PI控制器模块的无刷直流电机双闭环控制的仿真模型。仿真结果表明：基于多目标克隆选择算法优化的模糊控制器，能够系统具有上升时间短，无超调，稳态误差小等优势，具有较强的鲁棒性和自适应性。
[Abstract]:Brushless DC motor (BLDCM) is widely used in the field of electromechanical energy conversion due to its advantages of small volume, high power density, simple structure and better speed regulation performance. Because of the multivariable, strong coupling and nonlinear characteristics of brushless DC motor, the traditional PID control method is very difficult to find suitable PID parameters online, and it is difficult to achieve ideal results. The adaptive control algorithm is relative to the linear model for the identification of parameters and the prediction of motor state, but it is difficult to satisfy the accuracy and real-time performance of the nonlinear control object. Fuzzy control does not require mastering the exact model of the controlled object, but the subjective expert control rules can not meet the control requirements of different objects. The Clone selection algorithm can effectively search the global optimal solution and avoid falling into the local optimal solution. In this paper, a fuzzy adaptive PID controller based on multi-objective clonal selection algorithm is designed by combining various control strategies. In this paper, the composition and operation principle of brushless DC motor are briefly introduced, the mathematical model of brushless DC motor is studied, and the model of double closed loop control system of brushless DC motor is established. The outer loop speed regulation module of the system uses the multi-objective clone selection algorithm to optimize the fuzzy adaptive PID controller, while the inner loop regulator adopts the traditional Pi controller. This paper presents two methods to optimize fuzzy control rules by using multi-objective clonal selection algorithm and two fuzzy control methods based on elitist oriented mechanism. These two optimization methods can improve the control performance of brushless DC motor: compared with the conventional controller, the response time of the system is improved, and the stability can be achieved quickly. Compared with the conventional controller, it has higher control precision. Only the multi-objective clonal selection algorithm can optimize the control rules of fuzzy adaptive PID controller, although it can obtain the overall optimal control rules, but it can not meet the preferences of decision makers. The controller with elitist strategy can search the optimal solution of Pareto quickly and efficiently according to the decision preference. The simulation model of double closed loop control of brushless DC motor including main circuit module logic commutation module speed loop module and current Pi controller module of brushless DC motor is built in Matlab2012/Simulink. The simulation results show that the fuzzy controller based on multi-objective clonal selection algorithm has the advantages of short rise time, no overshoot and small steady-state error, and has strong robustness and adaptability.
【学位授予单位】：河南科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP273.4;TM33

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