Title: Lyapunov-based MPC for nonlinear process with online triggered linearised model

Authors: Ruo Wu; Dongya Zhao

Addresses: College of New Energy, China University of Petroleum, Qingdao 266580, China ' College of New Energy, China University of Petroleum, Qingdao 266580, China

Abstract: Most of industrial processes are nonlinear. Model predictive control (MPC) using an explicit nonlinear model can achieve satisfactory performance, however, it will bring a high computational burden. Although linear MPC is widely used in practice, a linear model cannot deal with the highly nonlinear system dynamic that is well overall in a wide operating region. In this study, an error trigger rule evoking a re-modelling algorithm to re-linearise the known nonlinear analytical model has been proposed for closed-loop nonlinear systems with input constraints. The error-triggering can be conducted by an error quantiser that quantifies model error and the re-linearisation program is triggered when the accumulated error exceeds the set threshold. The stability of the process is maintained by using the Lyapunov-based MPC. The effectiveness of the proposed control algorithm is validated by a chemical process simulation.

Keywords: model predictive control; MPC; nonlinear systems; online linearisation; error-trigger; computation time.

DOI: 10.1504/IJAAC.2023.127319

International Journal of Automation and Control, 2023 Vol.17 No.1, pp.1 - 18

Received: 27 Apr 2021
Accepted: 01 Aug 2021
Published online: 30 Nov 2022 *