Lyapunov-based MPC for nonlinear process with online triggered linearised model Online publication date: Wed, 30-Nov-2022
by Ruo Wu; Dongya Zhao
International Journal of Automation and Control (IJAAC), Vol. 17, No. 1, 2023
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.
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