Model predictive control method of a parallel electromagnetic isolation system based on the improved genetic algorithm

Abstract

To improve the vibration isolation performance for a parallel electromagnetic isolation system, an improved genetic algorithm to optimize the Q and R matrices in the control objective function for a model predictive control approach is proposed. In this study, a parallel electromagnetic isolation system with two electromagnetic isolation units is designed to expand the vibration isolation range to isolate the large object. The dynamical equation and state equation of the parallel electromagnetic isolation system are built. The nonlinear relationship among electromagnetic force, coil current, and gap is calculated by COMSOL Multiphysics to design the model predictive control controller. Meanwhile, an improved genetic algorithm by the variable chromosome length coevolutionary method is presented to tackle two issues. The first issue is that the parameters of Q and R matrices in the control objective function are mainly selected by trial and error. The other issue is that the model predictive control approach needs to determine prediction steps which may lead to the model predictive control approach suffering from heavy computation or an inaccurate prediction model. Simulation and experimental results demonstrate that the parallel electromagnetic isolation system with model predictive control method based on the improved genetic algorithm can achieve better vibration isolation performance in comparison with the passive isolation system.