A short predictive Model Predictive Control (MPC) approach for hybrid characteristics analysis in DC-DC converter - Erliza, Serri (2017)
Historically, the MPC has been successfully applied in drives system for over a decade. Furthermore, the DC-DC converter naturally deals with high switching phenomenon that contributes to the challenging in control approach. Its operation conventionally associated with PI/PID controller in order to meet the desired output. However, the PI/PID controller lacking in getting a good transient response since this controller highly depends on the controller gains. Recently, an advanced controller has been proposed in the literature for the purpose to enhance the DC-DC converter performance. Hence, in this thesis, the short prediction horizon of MPC using search tree optimization that generates low switching states phenomenon is proposed. The MPC algorithm is developed based on the hybrid characteristic signals from the DC-DC converter. The load changes due to the increasing or decreasing the loads (could be happened of heating effect) will affect the tracking of the output voltage. The Kalman Filter (KF) is used for load estimation for smoothing and tracking the output voltage. The performance of short prediction horizons is being compared to PI controller in terms of transient response during the start-up scenario. The results show that the proposed controller has a better response than PI controller, which is the overshoot has been reduced to more than 50% and the settling time more faster about 25% than PI controller during start-up scenario. Therefore, this control approach for DC-DC buck converter has produced the promising output transient performance when compared with the conventional PI controller while also minimizing the switching sequence phenomenon.