An Improved Active Feedforward Compensation Based on Model Predictive Control for Electrical Test Bench

The load generator system is widely used to perform dynamic and static load tests of electric machines or combustion motors. Due to their simple structure and mature theory, classical control theories, such as the Root Lucas method and the state feedback method, have been widely used in power electronics and electric drives. However, in classical control strategies, controller parameters are designed by trial and error, which cannot achieve optimal design. As an optimal controller, Model Predictive Control (MPC) does not seek only control, but optimal control. MPC can be divided into continuous control set model predictive control (CCS-MPC) and finite control set model predictive control (FCS-MPC). Now, the fact that the dynamometer is a precision instrument and should have a wide operating range of speed-torque, in this paper based on the existent weakness in the MPC, An Improved Active Feedforward Compensation Based on Model Predictive Control proposed to provide better robustness against uncertainties caused by parameters mismatch between the plant model and the model used in the predictive controller. With the evaluation done with the help of simulation, visible performance improvement in the system occurs.