An offset-free MPC formulation for nonlinear systems using adaptive integral controller. (August 2019)

• Record Type: Journal Article
• Authors: Hermansson, A.W.; Syafiie, S.
• Is Part Of: ISA transactions. Volume 91(2019)
• Page Start: 66
• Page End: 77
• Publication Date: 2019-08
• Subjects: Model predictive control -- Nonlinear systems -- Multiple models -- Industrial control -- Offset-free control -- Adaptive control; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2019.01.037
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This paper investigates a novel offset-free control scheme based on a multiple model predictive controller (MMPC) and an adaptive integral action controller for nonlinear processes. Firstly, the multiple model description captures the essence of the nonlinear process, while keeping the MPC optimization linear. Multiple models also enable the controller to deal with the uncertainty associated with changing setpoint. Then, a min–max approach is utilized to counter the effect of parametric uncertainty between the linear models and the nonlinear process. Finally, to deal with other uncertainties, such as input and output disturbances, an adaptive integral action controller is run in parallel to the MMPC. Thus creating a novel offset-free approach for nonlinear systems that is more easily tuned than observer-based MPC. Simulation results for a pH-controller, which acts as an example of a nonlinear process, are presented to demonstrate the usefulness of the technique compared to using an observer-based MPC. Highlights: Combining model predictive control with classical integral controller. Achieves offset-free control without using observer or modeling of uncertainty. Nonlinear control of a pH system. Outperforms classical observer based techniques for test system.