A wavelet based speed controller for interior permanent magnet motor drives

摘要

The use of permanent magnet synchronous motors in high performance ac motor drives has increased recently due to advances in manufacturing and commercializing permanent magnet (PM) materials, power electronics, digital signal processors, and intelligent control algorithms. Among several designs of permanent magnet motors, the interior permanent magnet (IPM) synchronous motor, which has magnets buried in the rotor core, shows excellent properties such as robustness, rotor physical non saliency, and small effective air gap. Fast speed tracking, quick recovery of speed from disturbances, and insensitivity to parameter variations are some of the main criteria of the high performance drive (HPD) systems for applications such as automotive, aerospace, air conditioners, robotics, rolling mills, machine tools, etc. The IPM motor with a suitable speed controller can meet the required specifications of HPD systems. -- This work presents the development and implementation of a novel wavelet neural network (WNN) based self-tuning multiresolution proportional integral derivative (MRPID) controller for accurate speed control of the interior permanent magnet synchronous motor (IPMSM) drive systems under system uncertainties. In the proposed self-tuning MRPID controller, the discrete wavelet transform is used to decompose the speed error, which is the difference between the command speed and the motor measured speed, into localized sub-band frequencies established by the discrete wavelet transform (DWT). Such localized decomposition of the speed error signal produce sets of independent coefficients, which also contain information about the system dynamics, effects of external disturbances, measurement errors, noise, etc. Moreover, these wavelet transformed coefficients are scaled by their respective gains, and then are added to generate the control signal for the drive system. Initially, the analogy between the proportional integral derivative (PID) decomposition and the multiresolution decomposition of speed error is used in order to set the initial gains of the MRPID controller. Next the wavelet neural network (WNN) is used for self-tuning of the proposed MRPID controller to ensure optimal drive performances in real time under system disturbances and uncertainties. The learning rates of the WNN are derived on the basis of the discrete Lyapunov function in order to confirm the stability of the proposed self-tuning MRPID controller based IPMSM drive system. -- The minimum description length (MDL) data criterion and the entropy based criterion are successfully used to select an optimum mother wavelet function and to find the optimal levels of decomposition of the speed error signal, respectively of the proposed self-tuning MRPID controller. The comparative performances of the IPMSM drive system using the fixed gain proportional integral (PI) controller, proportional integral derivative (PID) controller, adaptive artificial neural network (NN) controller, and the proposed self-tuning MRPID controller are presented. The proposed self-tuning MRPID controller is found better than the conventional fixed gain and adaptive speed controllers. -- The performances of the proposed self-tuning MRPID controller are investigated in both simulation and experiments at different dynamic operating conditions of the IPMSM drive system. The flux weakening control scheme of the proposed self-tuning MRPID based IPMSM drive system is successfully implemented in real time using the dSPACE dsl 102 digital signal processor board on the laboratory 1-hp IPM motor. The performances of the proposed drive system are also compared with the fixed gain PI controller based drive system in real time in order to verify the superiority of the proposed self-tuning MRPID controller over the conventional controllers. The simulation results and laboratory test results confirm the effectiveness of the proposed self-tuning MRPID controller as a robust controller for high performance industrial motor drive systems.