【作者】 刘丹；

【导师】 夏长亮；

【作者基本信息】 天津大学 ， 电机与电器， 2007， 硕士

【摘要】 无刷直流电机(Brushless DC motor,简称BLDCM)的功率密度和效率都比较高,是一种性能优越、应用前景广阔的电机。由于控制策略的优劣对电机的性能影响很大,随着各种应用场合对无刷直流电机控制性能要求的提高,对调速系统控制策略的研究成为无刷直流电机目前研究的一大热点。无刷直流电机是一种多变量、非线性的控制系统,采用经典的PID控制难以得到满意的控制效果。本文将免疫反馈机理应用于无刷直流电机的转速控制中。首先提出了一种新型的基于模糊规则的免疫PID控制器。这种免疫PID控制根据T细胞的生物免疫反馈机理,包括决定应答速度的激活环节和决定稳定效果的抑制环节。用模糊规则来逼近其中的抑制环节,并与PID结合补偿其控制偏差。系统采用双闭环控制,内环为电流环,外环为速度环。然后在MATLAB环境中进行系统仿真,在已搭建好的无刷直流电机模型基础上建立模糊免疫PID控制系统。仿真结果表明,较之传统PID控制,该控制系统超调量小,速度响应快,而且速度响应受电机参数变化影响小,各种外界干扰也得到了很好的抑制,具有较高的控制精度和较好的鲁棒性。此外,本文还研究了将免疫反馈机理应用于无刷直流电机神经网络控制时的自适应学习中,加快了神经网络的收敛速度,神经网络学习的优化取得了较好的效果。实验方面以TI公司的数字信号处理器TMS320F2812 DSP为基础对无刷直流电机控制系统进行设计。本文从硬件和软件设计两方面进行介绍,该硬件系统包括驱动电路设计、逆变电路设计、采样电路和串行通讯等几个部分,并通过软件设计最终实现了换向、PWM控制、速度调整等。此外本文还简述了应用无刷直流电机作为门机的电梯门控制系统。最后通过实验证实了该无刷直流电机系统达到了较好的控制效果。更多还原

【Abstract】 The brushless DC motor (BLDCM) has been widely used because of its excellent performance such as its particularly high mechanical power density and effectiveness. However, because of the even high requirement of many applications, many researches are focused on the improving of the control strategy of the BLDCM.The brushless DC motor is a multi-variable and non-linear system. Conventional PID control can not obtain satisfied control effect. This dissertation presents a novel control approach for BLDCM by using artificial immune feedback mechanism. It introduces an immune feedback PID controller based on fuzzy model. This novel controller is inspired by the biological immune feedback mechanism based on the functioning of T-cells, including an active term, which controls response speed, and an inhibitive term, which controls stabilization effect, and we employ a fuzzy logic to implement the inhibitive term. Then we combine it with PID control to make sure that the control can be not only self-tuned but also without static error. Also, the system includes current and velocity closed loops. The whole BLDCM control system is simulated by MATLAB. Based on the BLDCM model, the simulation of fuzzy immune PID control system is then set up. The result of the simulation illustrates that excellent flexibility and adaptability as well as high precision and good robustness are obtained by the proposed strategy, especially when compared to PID control strategy. In additional, an ANN controller based on immune feedback adaptive learning is presented, and the controller is applied into the BLDCM velocity control. This application of the immune feedback law to the tuning of learning rate improves the gradient descent NN learning algorithms and speeds up the convergence significantly.For experiment, the research designs the control system of BLDCM based on TMS320F2812 DSP (Digital Signal Processor). This design divided into software and hardware design, which include drive and inverter circuit, sampling circuit, serial communication, PWM control and speed regulating. Also, an elevator door motor system using BLDCM is introduced. Lastly, the experiment results prove that the control system has excellent performance.更多还原