【作者】 朱晓琳；

【导师】 王永骥； 沈安文；

【作者基本信息】 华中科技大学 ， 控制理论与控制工程， 2007， 博士

【摘要】 近年来,随着电力电子器件、微处理器和电机控制理论的飞速发展,感应电机在运动控制中得到了广泛应用,与此同时,人们对其性能要求也越来越高。一方面,由于感应电机矢量控制系统会受电机参数变化以及外部负载扰动等因素的影响,单纯采用传统控制方法难以满足高性能的控制要求,因此需要谋求新的控制策略以使系统对这类影响具有较好的鲁棒性。另一方面,在转速闭环控制的高性能感应电机矢量控制系统中,速度传感器的安装给系统带来了一些不利的影响。研究无速度传感器控制系统并改善其性能,成为交流传动领域的研究热点。本文考虑到神经网络在非线性、复杂系统以及不确定系统的控制与辨识方面优越的性能,将它分别与PI控制以及模型参考自适应控制相结合,应用到感应电机有速度传感器和无速度传感器矢量控制系统中,改善了系统的性能。首先,在有速度传感器矢量控制系统中,为解决在系统参数发生较大变化等情况时,采用常规PI控制器难以获得满意的控制性能的问题、减轻系统参数变化和外部负载扰动对系统产生的不良影响,本文根据神经网络和矢量控制系统的特点,将神经网络控制理论分别与PI控制和模型参考自适应控制相结合,组成了具有自适应能力的神经网络PI速度控制器和神经网络模型参考补偿控制器,实现了矢量控制系统对电机参数及负载转矩等的自适应控制。在神经网络模型参考补偿控制器的具体应用中,为提高网络的映射能力和矢量控制系统的动态响应能力,本文在控制器中采用三层前向神经网络结构,并用广义PID神经网络自适应算法修正网络权值,采用这种算法在一定程度上加快了网络的收敛速度。随后,在无速度传感器矢量控制系统中,本文探讨了基于神经网络PI控制器的磁链观测方法、定子电阻在线辨识方法和转速估算方法。在转子磁场定向的无速度传感器矢量控制系统中,为保证矢量解耦控制的有效实现,需要进行精确的磁场定向。在众多磁通观测方法中,转子磁链的电压模型和电流模型各有优缺点且在很多方面具有互补性,因此很多方法利用电压-电流组合模型进行磁链观测。本文在静止坐标系下,探讨了一种基于神经网络PI控制器的电压-电流组合模型闭环磁通观测方法,通过电流模型对电压模型的自适应修正作用将二者结合起来完成转子磁链的观测过程,解决了电压模型观测器的不稳定问题。在组合模型闭环磁链观测器中,随温度变化的定子电阻变化会直接影响转子磁链的观测效果,进而影响转速的控制精度,破坏系统的性能。为此,本文探讨了用神经网络PI控制器实现的定子电阻在线辨识方法,通过定子电阻在线辨识提高了转速控制精度。在无速度传感器矢量控制系统的核心问题——转速估算环节中,为改进基于PI控制作用构造转速信号的系统的性能,本文探讨了一种基于神经网络PI控制器的转速估算方法。该方法将定参数PI调节器的PI参数直接作为神经网络权值,在输入偏差变化时,通过对权值PI参数的自适应调整,加快了系统的收敛速度、减小了超调量和稳定时间。最后,本文在基于TMS320F2808的感应电机矢量控制系统实验平台上,进行了有速度传感器和无速度传感器矢量控制实验。在有速度传感器矢量控制实验中,采用神经网络PI速度控制器进行转速调节,证实了控制方案的有效性和可实现性。对于无速度传感器矢量控制,用估算转速进行闭环控制,验证了本文探讨的基于神经网络PI控制器的转速估算方法能有效地改善系统的动态性能,在突加负载扰动和低速运行时能保证系统稳定运行,同时验证了采用的逆变器死区电压补偿方法能有效改善电流波形,改进系统性能。更多还原

【Abstract】 In recent years, induction motor has been extensively used in motion-control field with the advances in power devices, micro-processor and motor control theory. At the same time, its high performance is required increasingly. On one hand, as the performance of induction motor vector control system will be influenced by parameter variations, external load disturbances, etc., the high performance requirement can’t be met simply using traditional control methods. To make the system be robust to the influences, new control strategies have to be developed. On the other hand, the mounting of speed sensors brings some disadvantages to the induction motor vector control system in which rotor speed is closed-loop controlled. Therefore, the research to speed-sensorless drive system and enhancing its performance has become a popular research aspect in AC drive field. Considering the superior performance in the control and identification to nonlinear, complex and uncertain systems, neural networks was used in induction motor vector control system combined with PI control and model reference adaptive control in this dissertation, which has improved system performance.At first, in order to solve the problem that satisfactory performance can’t be achieved using conventional PI controller when the system parameters vary in a wide range and reduce the influence of parameter variations and external load disturbances, the adaptive neural network PI speed controller and neural network model reference compensation controller were used in induction motor vector control system in the dissertation. The controller was constructed according to the characteristic of neural networks and vector control system and combining neural network with PI control and model reference adaptive control. Then, adaptive control to motor parameters and the load torque was realized.In the application of the neural network model reference compensation controller, to enhance the mapping capability and dynamic response capability of the system, a three-layer forward neural network was used in the controller and adaptive control algorithm with generalized PID was used to correct the weights of the network. In a certain extent, the speed of convergence was accelerated by using the algorithm.Subsequently, the flux observation method, stator resistance on-line identification method and speed estimation method based on the neural network PI controller were presented in the speed-sensorless vector control system.In the rotor flux-oriented system, in order to guarantee the effective decoupling of vector control, the precise flux orientation is needed. Among many flux observation methods, voltage model and current model have their own advantages and disadvantages, and they are complementary in many ways, so they were combined in many flux observers. A closed-loop flux observation method using voltage-current combination model based on the neural network PI controller was presented in the dissertation, which realized the rotor flux observation and solved the instability of voltage model through the current model’s adaptive correction to the voltage model in stationary axes,.In closed-loop flux observer using combination model, the stator resistance’s error by the temperature’s variation will impact on the accuracy of rotor flux observer and speed control, destroy the performance of the system. Therefore stator resistance on-line identification method based on neural network PI controller was presented, by which the accuracy of speed control was improved.About the core of the speed-sensorless vector control system, that is speed estimation, in order to improve the performance of the system, in which the speed signal is structured with PI control, a speed estimation method based on the neural network PI controller was presented. In the method, the PI parameters were set as the weights of the neural network. When the input error varied, the weights were adaptively regulated, by which the convergence rate was accelerated, at the same time, the overshoot and stabilization time were reduced.Finally, the experiments were done in the induction motor vector control system platform based on TMS320F2808. In the system with speed sensor, neural network PI controller was used for speed control. The experiment results verify the program is valid and feasible. In the system without speed sensor, the estimated speed is closed-loop controlled. The experiment results verify that system’s dynamic performance can be improved by using the speed estimation method based on neural network PI controller presented in the dissertation, the stability of the system was guaranteed in sudden increase in load disturbance and low-speed operation, the current waveform and system performance can be improved effectively by using the dead zone inverter voltage compensation method.更多还原