Testing of genetic-PI based controller for IPMSM drive

Abstract

This paper presents a laboratory testing of genetic algorithm (GA) based self-tuned PI controller far the speed control of interior permanent magnet synchronous motor (IPMSM). A radial basis artificial neural network function is used for on-line tuning of the PI controller. CA has been used in this work in order to obtain the optimized values of the PI constants for precise speed control. An performance index has been developed using GA, whose minimum value ensures zero steady-state error, minimum speed deviation and minimum settling time of the IPMSM drive. The initial values of the radial basis function network (RBFN) are obtained through off-line learning. Training data for off-line learning are generated hy simulating the IPMSM drive under various operating conditions and uncertainties. For on-line implementation, the PI constants are tuned hy updating lhe parameters of the RBFN maintaining the genetic performance index at its minimum value. In real time implementation, lhe proposed controller has been realized using a digital signal processor (DSP) board DSllOZ for a lhp laboratory IPMSM. The control algorithm is written in C++, compiled and then down loaded to the DSP hoard. The agreement between the simulation and test results confirms the effectiveness of the proposed controller for the vector control of the IPMSM.

Intelligent Speed Control of Interior Permanent Magnet Motor Drives

“…These drives exploit the well-known ability of the ANN to handle nonlinear system uncertainties such as step change in command speed, load impact, saturation and parameter variations [13]. Genetic algorithms have also been incorporated in combination with ANNs to tune PI controller parameters to achieve greater insensitivity to load and parameter variations [14,15]. Genetic algorithms have also been used in tandem with a FLC [16].…”