Iterative EKF as a controller in novel MPC formulation: First principles model based IEKF-MPC for SISO systems

In the present study, novel MPC control strategy, using first principles models and complete state infor-mation is formulated using iterative EKF as predictive controller. This is based on extension of iterative EKF control estimation concept to MPC, instead of solution of an optimization problem approach fol-lowed in conventional MPC. The proposed approach incorporates all aspects of MPC including feedback correction term to handle process-model mismatch, move-suppression factor for control effort minimiza-tion, and constraints handling capability including terminal constraints. The performance of the proposed control strategy is evaluated through simulation case studies of SISO anaerobic digestion process and isothermal methyl methacrylate polymerization reactor with relative degree 1 and 2, respectively. The proposed strategy has shown superior performance over standard MPC in terms of faster response, set point tracking and disturbance rejection in both the case studies. Also, the proposed approach is found to be computationally more efficient than the standard MPC. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

In this study, a novel MPC control strategy using first principles models and complete state information is developed based on iterative EKF as a predictive controller. The proposed strategy demonstrates superior performance in handling process-model mismatch, minimizing control effort, and handling constraints compared to standard MPC.