High precision optimum control of a linear motor drive using genetic algorithm

Abstract

New methods of optimizing the performance of a brushless DC linear drive for high precision have been developed in this research. Model-based predictive control approach is first developed to control the position and the speed to track the reference profiles. The system performs well for movement that is above the millimeter range. Due to the frictional effect, the system fails in the micrometer range. Therefore, friction model has been included in the system model for optimization and compensation. Unlike most friction compensation approaches, that use the static friction model, this thesis uses the dynamic friction model that can describe both static and friction dynamics such as stick-slip, pre-sliding displacement etc. As the model is nonlinear, a new technique for identifying the LuGre friction parameters using the genetic algorithm is developed. This offline technique is superior than the conventional method as the global solution is obtainable. The mathematical derivation of the MPC controller gain vectors becomes impossible when the nonlinear friction model is included in the system. A new offline technique for optimizing the controller using the genetic algorithm has been developed. The experimental results have shown a significant improvement of the performance as compared to the conventional MPC approach especially in the micrometer range.