Model Predictive Control of Nonlinear System with Event-Triggered Parametric Identification

This paper focus on the Model Predictive Control (MPC) for nonlinear systems where some parameters of the system dynamic may change because of the external disturbance or environmental changes in the industrial process. For this problem, a Lyapunov-Based MPC with a parametric online update is proposed in this paper, which can promise the system stability. To capture the parameter change through the system output and better control the system according to this, an event-triggered parametric estimation mechanism is proposed. At each time instant, according to the difference between historical measured value and prediction value of the state, the controller will judge whether the parameter identification mechanism is needed to be triggered to update the system parameters. When the parametric estimation mechanism is not executed, the cost function should be optimized to derive a good profit. If the predicted values of the system parameters are out of date and differ a lot from their current true values, the MPC is supposed to update the parameters and meanwhile promise that in this process, the closed-loop system state is always maintained within a predefined stable region. A simulation of an industrial process shows the effectiveness of our method.