Adaptive Simplified Model Predictive Control with Tuning Considerations

Model predictive controller is widely used in industrial plants. Uncertainty is one of the critical issues in real systems. In this paper, the direct adaptive Simplified Model Predictive Control (SMPC) is proposed for unknown or time varying plants with uncertainties. By estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly calculated. The proposed method is validated via simulations for both slow and fast time varying systems. Simulation results indicate the controller ability for tracking references in the presence of plant’s time varying parameters. In addition, an analytical tuning method for adjusting prediction horizon is proposed based on optimization of the objective function. It leads to a simple formula including the model parameters, and an indirect adaptive controller can be designed based on the analytical formula. Simulation results indicate a better performance for the tuned controller. Finally, experimental tests are performed to show the effectiveness of the proposed methodologies.