基于参数辨识及补偿控制的异步电动机智能控制技术研究
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摘要
本论文以转子磁场定向控制的异步电动机调速系统为研究对象,以提高系统的动态响应和鲁棒性为主要目标,研究和建立转子电阻等参数变化情况下的异步电动机动态数学模型,以及基于此动态数学模型的参数辨识、模糊神经网络控制、神经网络控制、非线性控制和自适应控制等控制策略、控制结构和控制技术。
1.在实时控制中,参数在线辨识算法的复杂程度直接影响系统控制的实时性和动态性能,而参数变化情况下系统的数学模型,不仅能够用于分析参数变化对系统性能的影响,而且对提高辨识算法的快速性和准确性有重要的作用。为此本文在异步电机动态数学模型基础上,应用状态增量的方法,研究、推导和建立了异步电机转子电阻和电感变化情况下的数学模型,并应用该模型分析了参数对系统性能的影响。模型以Laplace算子的传递函数形式表示,具有结构简单、物理概念清晰、使用方便等特点,为研究分析电机转子电阻等参数对系统性能的影响和参数辨识等提供了一种新的简单实用的方法。
2.应用人工神经网络的方法,在所建立的状态增量型动态数学模型研究的基础上,提出了转子电阻、系统转动惯量、粘性摩擦系数和负载转矩的辨识方法,在此基础上通过在前向通道串入一个校正装置的方法,提出了转子电阻和负载转矩扰动影响的补偿和校正的方案,辨识方法和校正方案具有结构简单、使用方便的优点。同时应用非线性控制理论,还提出了一种转子电阻和负载转矩参数辨识的方法,在此基础上设计和建立了非线性控制器和自适应控制系统,实现了良好的系统跟踪响应性能。
3.提出了一种以转矩电流为指导值和目标函数的神经网络控制方案,建立了考虑转子电阻变化情况下进行补偿控制的系统。在建立输出参考模型和教师指导控制器的基础上,推导和建立了以转矩电流为目标函数和以电机输出转速为目标函数的两种神经网络控制方案的计算控制算法,在电机的动态响应和参数变化对系统的影响方面进行了研究分析。计算机仿真结果证明,转子电阻补偿器能有效地改善控制系统的动态响应,以
The speed regulating system of vector control on induction machine is chosen as the object of study in this dissertation. The main aim of study is to improve the performances of the system in dynamic responses and robust. Starting with the dynamic mathematical model study in the conditions of parameter variations, the study is deeply done on the control structures and control strategies of parameter identification, fuzzy-neural control, neural control, nonlinear control and adaptive control.
In real time control, the complexity degree of online parameter identification algorithm directly affects the performances of real time system and dynamic responses. The mathematical models in the condition of parameter variations are not only used to analyze the effects of parameters on system performance, but also play the important role in improving the celerity and veracity of identification algorithms. Therefore, the dissertation presents the mathematical models considering rotor resistance and inductance variation the state increment method. Because the models have the formats of Laplace transfer function, they have the advantages of simple structure, clear concept and feasible application, which supply some new and simple methods to study the vector control of induction machine.
Based on the above research, some identification methods of rotor resistance, moment of inertia, viscous coefficient and load torque are presented by using neural network. The projects for the compensations of rotor resistance variation and load torque disturbance are covered. In order to realize good tracking performance, this dissertation also establishes a non-linear controller and adaptive system based on a new identification method of rotor resistance variation and load torque disturbance with non-linear control theory.
A novel neural network control strategy with torque current teacher controller and target function is proposed. In addition, the new control system including neural network controller
and rotor resistance variation compensation is also built. The control algorithms of the system for both torque current target function and motor speed target function are studied. The computer simulation results parameter variations and dynamic responses show that the system has strong robustness and good dynamic performances. After the research of neural network controller, we deeply study the fuzzy-neural network controller and combine it with compensation unit of rotor resistance variation to form the intelligent control system. The controller has the regulation functions of the center point parameters and width parameters of membership functions to improve the learning ability. The simulation results demonstrate the validity and good robustness of this scheme. In order to study above controllers, the methods using torque current target function are compared with the traditional method using motor speed target function under both neural network controller and fuzzy-neural network controller. Four control algorithms are proposed and discussed. Both theoretical analysis and computer simulations prove that the intelligent controllers using torque current target function have better performances in dynamic accuracy.
1.在实时控制中,参数在线辨识算法的复杂程度直接影响系统控制的实时性和动态性能,而参数变化情况下系统的数学模型,不仅能够用于分析参数变化对系统性能的影响,而且对提高辨识算法的快速性和准确性有重要的作用。为此本文在异步电机动态数学模型基础上,应用状态增量的方法,研究、推导和建立了异步电机转子电阻和电感变化情况下的数学模型,并应用该模型分析了参数对系统性能的影响。模型以Laplace算子的传递函数形式表示,具有结构简单、物理概念清晰、使用方便等特点,为研究分析电机转子电阻等参数对系统性能的影响和参数辨识等提供了一种新的简单实用的方法。
2.应用人工神经网络的方法,在所建立的状态增量型动态数学模型研究的基础上,提出了转子电阻、系统转动惯量、粘性摩擦系数和负载转矩的辨识方法,在此基础上通过在前向通道串入一个校正装置的方法,提出了转子电阻和负载转矩扰动影响的补偿和校正的方案,辨识方法和校正方案具有结构简单、使用方便的优点。同时应用非线性控制理论,还提出了一种转子电阻和负载转矩参数辨识的方法,在此基础上设计和建立了非线性控制器和自适应控制系统,实现了良好的系统跟踪响应性能。
3.提出了一种以转矩电流为指导值和目标函数的神经网络控制方案,建立了考虑转子电阻变化情况下进行补偿控制的系统。在建立输出参考模型和教师指导控制器的基础上,推导和建立了以转矩电流为目标函数和以电机输出转速为目标函数的两种神经网络控制方案的计算控制算法,在电机的动态响应和参数变化对系统的影响方面进行了研究分析。计算机仿真结果证明,转子电阻补偿器能有效地改善控制系统的动态响应,以
The speed regulating system of vector control on induction machine is chosen as the object of study in this dissertation. The main aim of study is to improve the performances of the system in dynamic responses and robust. Starting with the dynamic mathematical model study in the conditions of parameter variations, the study is deeply done on the control structures and control strategies of parameter identification, fuzzy-neural control, neural control, nonlinear control and adaptive control.
In real time control, the complexity degree of online parameter identification algorithm directly affects the performances of real time system and dynamic responses. The mathematical models in the condition of parameter variations are not only used to analyze the effects of parameters on system performance, but also play the important role in improving the celerity and veracity of identification algorithms. Therefore, the dissertation presents the mathematical models considering rotor resistance and inductance variation the state increment method. Because the models have the formats of Laplace transfer function, they have the advantages of simple structure, clear concept and feasible application, which supply some new and simple methods to study the vector control of induction machine.
Based on the above research, some identification methods of rotor resistance, moment of inertia, viscous coefficient and load torque are presented by using neural network. The projects for the compensations of rotor resistance variation and load torque disturbance are covered. In order to realize good tracking performance, this dissertation also establishes a non-linear controller and adaptive system based on a new identification method of rotor resistance variation and load torque disturbance with non-linear control theory.
A novel neural network control strategy with torque current teacher controller and target function is proposed. In addition, the new control system including neural network controller
and rotor resistance variation compensation is also built. The control algorithms of the system for both torque current target function and motor speed target function are studied. The computer simulation results parameter variations and dynamic responses show that the system has strong robustness and good dynamic performances. After the research of neural network controller, we deeply study the fuzzy-neural network controller and combine it with compensation unit of rotor resistance variation to form the intelligent control system. The controller has the regulation functions of the center point parameters and width parameters of membership functions to improve the learning ability. The simulation results demonstrate the validity and good robustness of this scheme. In order to study above controllers, the methods using torque current target function are compared with the traditional method using motor speed target function under both neural network controller and fuzzy-neural network controller. Four control algorithms are proposed and discussed. Both theoretical analysis and computer simulations prove that the intelligent controllers using torque current target function have better performances in dynamic accuracy.
引文
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