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Internal Model Adaptive Control of Stator Current of Asynchronous Motor and Its Realization Zhuang Shengxian (Dr. Ultrasonic Electronics Co., Ltd. Postdoctoral Research Station? 515041) Chen Yongxiao (Department of Electrical Engineering, Zhejiang University, Hangzhou, 027) Li Rongji (Decade of Microelectronics, University of Electronic Science and Technology of China) , 610054) The principle of mode control (I) is to design the asynchronous motor current regulator, and the robustness of the I current regulator is analyzed by the matrix singular value. Then the model parameters are identified by the least squares method and finally applied to the rotor field orientation of the asynchronous motor. Vector control. The good performance of the adaptive I current regulator is verified by the simulation of the current regulator transfer matrix function and the asynchronous motor vector control operation experiment realized by DSP.
In the rotor motor field oriented induction motor vector control, the control decoupling of torque and flux is primarily dependent on the estimate of the rotor flux position. However, the control performance of the torque is also affected by the current regulation. In the voltage type PW control mode, the reference voltage output to the stator is obtained by decoupling the magnetic field direction and the given excitation current and torque current components are respectively adjusted by two independent PIs. However, due to the cross-coupling of the excitation current and the torque current component in the stator voltage equation, both in the synchronous rotating coordinate system and in the stator stationary coordinate system, the adjustment of the torque current is affected by the excitation current [1, 2] The general solution is to add a decoupling term from the motor input voltage command to cancel the coupling effect of the torque excitation current, or use state feedback and predictive control to improve the current or torque control performance but achieve more complex internal model control. (I) Design the current regulator of the induction motor and apply it to the current control of the permanent magnet synchronous motor [6]. In this paper, the stator current regulator is designed based on the internal model control method for the stator voltage equation of the asynchronous motor under rotor field orientation. The parameters of stator resistance and stator leakage inductance in the internal model controller are corrected by the least squares identification method of multivariable system parameters. Through the simulation analysis of the asynchronous motor I current regulator and the vector control system with DSP, the design of the I regulator has the advantages of less dependence on the motor parameters, easy adjustment, and better torque and current regulation. Rod and dynamic response performance.
Control Theory and Application 2 I-based Asynchronous Motor Stator Current Regulator Sets the stator voltage equation of the asynchronous motor in the synchronous flux of the rotor flux: the resistance and self-inductance of the stator, L is the self-inductance of the rotor, L is fixed Mutual inductance between the rotors. ω is the synchronous angular velocity of the magnetic field, and p is the differential operator.
Assuming that the rotor flux Χ is kept constant during the speed regulation of the motor, there is p Χ to obtain the transfer function matrix model of the current and voltage of the asynchronous motor: the structure of the internal model controller is shown in Figure 1. According to the design method of the internal model control, when the prediction model G(s) of the object is known, as is the robustness.
Where F(s) is a diagonal matrix feedforward low-pass filter to improve the robustness of the system. It can be known from equation (4) that the current transfer function of the asynchronous motor has no right half plane zero point and is approximated as a step system at high frequencies. Therefore, the feedforward low-pass filter F (s ) in (5) can be selected as: where I is an identity matrix.
For the order system, the relationship between λ and the rise time of the step response is approximately t ≈2 .2/ λ. The designed I current regulator Q (s ) can be obtained from equations (4) and (5) as: where R 1 , σ is an estimate of the stator resistance, self inductance, and leakage inductance. The I current regulator is equivalent to the feedback controller shown in Figure 1 (b): From equation (8), the current I control with the feedback controller is equivalent to the decoupled PI control. The integral term in the corner element forms a decoupling network. From Fig. 1, the closed-loop transfer matrix function can be obtained as follows: where G(s)=(s)G(s) is the open-loop transfer matrix function of the system.
Assuming that the change of a parameter in the motor parameter θ=[ R ] is Δθi, the output change caused by it is: where the derivation is advanced, the equation (10) can be expressed as: where the frequency domain theory according to the multivariate system has : Control Theory and Application σ(G (j ω)), σ(G (j ω)) are the minimum and maximum singular values ​​of the transfer function matrix G (j ω), respectively. It can be seen from equation (12) that the change in the output caused by the change in the transfer function matrix G (j ω) parameter is related to the minimum and maximum singular values ​​of G (j ω). Figure 2 is the maximum singular value curve of G (j ω) for ω = 500 (rad/s). The maximum singular value curve of G (j ω) at 100 is similar to ω and does not change much in all frequency ranges. When the maximum singular value of ωG (j ω) is abrupt in the high frequency band, the larger the ω is, the larger the change is. This indicates that the more sensitive the parameter changes, the better the robustness of the regulator, but the maximum in the fundamental frequency range (50 Hz). The singular value is small.
3 Internal model controller parameters online identification (Online identi I The performance of the current regulator depends on the prediction of the internal model and the actual model of the motor. Due to factors such as heat and magnetic saturation, the motor parameters will be generated during the operation. Large changes. To ensure the good performance of the I current regulator, it is necessary to correct the model parameters online and correct the I current regulator.
By performing z-transform on equation (4), we can find that the discretized current transfer matrix function is: where s is the root of the characteristic polynomial of the transfer matrix function (4), and T is the sampling period.
The above transfer matrix function can be described as two two-input single-output subsystems, each of which can be parameterized by SISO process [7]. The input of the first subsystem is u is i (k ), and the parameter to be identified is [ a parameter to be corrected in the a I current regulator is R 1 , which is known by the subsystems after decomposition. If the influence of noise is not considered, the parameter identification recursive algorithm of the first subsystem can be obtained according to RLS. The motor parameter to be identified according to the coefficient in equation (13) is 4 adaptive I current regulator implementation. (Iple from equation (8) shows that the control voltage derived from the I current regulator can be decomposed into: where i (s ) is the reference stator current and i(s ) is the actual stator current.
Convert equation (19) into a state equation and discretize it: where is the sampling frequency of the current control loop and k is the sampling instant.
The internal model adaptive control and implementation of the stator current of the 4th asynchronous motor is known from equation (19). The I current regulator can be realized by independent PI regulation plus internal feedback (as shown in Figure 3), and the right term is PI regulation. The second term is internal feedback, while equations (20) and (21) give their implementation. In order to verify the good performance of the I current regulator, we use the DSP (TS 50 ) and vector control coprocessor (AD 200) to form a vector control system for the rotor field orientation of the asynchronous motor. The system consists of current feedback, speed feedback, and speed control. And adaptive I current control and other components. Speed ​​regulation, I current regulation, internal model parameter identification and other operations are performed by the DSP, current sampling and A/D conversion, three-phase stator (ab) coordinate system to synchronous rotation (T) coordinate system transformation and its inverse The conversion, generation of three-phase PW, and the like are all performed by the AD 200. The current sampling frequency ω ≥ 10λ is required for the I current regulator. The current sampling frequency selected in the experiment is 8 kHz, and the sampling frequency of the rotational speed is 500 Hz. The identification of the model parameters in the current regulator is determined by the least squares method, and the output voltage u is used as the input identification signal, and the injection of the PRBS is synchronized with the current sampling. Ensure that the output is slower than the input. Appropriate selection of the initial value of the matrix P allows the identification parameters to converge faster. In the experiment, P (0 )= 100I is selected, and the initial parameter of the motor is [R. The estimation process is shown in Fig. 4, and the convergence value is 4.792 Ψ. Figure 5 shows the step response curve of the I current regulator when the model is matched. When λ = 0, the current response rise time is less than 1 s. Figure 6 shows the output torque waveform when the load is increased or decreased. Given a speed of 300 rad/s, the output torque and speed have a fixed impact when the load is abrupt. However, the changes are small, the adjustment time is not long, and the dynamic stability is good.
5 Conclusions This paper applies the internal model control method to the current regulation in asynchronous motor vector control. The design of the I regulator is less dependent on the motor parameters and only needs to be adjusted with the rise time of the closed loop system.
Identifying the stator resistance and leakage inductance of the motor allows online calibration of the parameters of the I regulator. The control structure with PI adjustment plus internal feedback is easy to implement by DSP. The simulation and experiments show that the adaptive I regulator has good dynamic response of current and torque, and has little sensitivity to parameter changes. It is suitable for rotor field oriented vector control of asynchronous motor powered by voltage type PW inverter.
Control Theory and Application [12] Cheng Chuwang, Sun Youxian. State feedback controller for time-varying uncertainties systems [13] Deng Feiqi, Feng Zhaoshu, Liu Yongqing. A method for designing robust controllers for linear time-delay systems based on RiatiIto equation [J]. Control Theory and Applications, 1997, 14 (6):887 [14] Liu Yongqing, Feng Zhaoshu. Theory and application of large power systems [ ] . Volume 4 : Random?
Stability and control. Guangzhou: South China University of Technology Press, 1992 Author brief introduction Guan Xinping was born in 1963. Professor, Ph.D. Published more than 50 academic papers. The current main research directions are the qualitative theory of time-delay systems, robust control theory, discrete distributed parameter systems, linear systems and the application of modern control theory in the aerospace field.
Lin Zhiyun was born in 1976. master. The current main research directions are the qualitative theory of time-delay systems, robust control theory, generalized system theory and the application of modern control theory in the aerospace field.
Duan Guangren was born in 1962. Professor, doctoral tutor. The current main research directions are linear system theory, robust control theory, generalized system theory and modern control theory in the aerospace field. Published more than 130 academic papers.
[7] Fang Chongzhi, Xiao Deyun. Process Identification [ ] . Beijing: Tsinghua University Press, 1988 Author of this article Zhuang Shengxian was born in 1964. He received his master's degree from Southwest Jiaotong University in 1991 and his Ph.D. from the University of Electronic Science and Technology in 1999. Now I am doing postdoctoral research at the Electrotechnics Station of Zhejiang University. The research direction is adaptive control and intelligent control of micro-motors and their transmission systems.
Chen Yong School was born in 1930. Professor, doctoral tutor, director of Zhejiang University Motor and its Control Research Institute. The research direction is adaptive control and intelligent control of micro-motors and their transmission systems.
Li Rongji was born in 1940. He graduated from Chengdu Telecommunications Engineering College in 1963. He is currently the director, professor and doctoral supervisor of the Institute of Microelectronics of the University of Electronic Science and Technology. The research direction is high-voltage intelligent power control and power integrated circuit design in power electronic circuits.
Control theory and application
Internal Model Adaptive Control and Implementation of Stator Current of Asynchronous Motor