RBF-ARX model-based MPC approach to inverted pendulum: An event-triggered mechanism

Abstract

The event-triggered predictive control based on RBF-ARX model (state-independent autoregressive with exogenous inputs constructed by radial basis function network) is designed in this paper, which is aiming at the issue of accumulating computational burden associated with solving the optimization problems at each sampling time. To alleviate the computational burden while the performance of model predictive control (MPC) cannot be simultaneously sacrificed, from the perspective of model building, the data-driven RBF-ARX model presented by a new state space form is combined into the event-triggered model predictive control (ETMPC) method instead of the mechanistic model. Therefore, we attempt to expand the results of ETMPC to the complex industrial processes rather than the restricted model described by mechanism in terms of proposing a new event-triggering condition (ETC) with a data-driven model. Additionally, the stability analysis of RBF-ARX model-based ETMPC is demonstrated with a focus on the boundedness of the model’s output. The simulation results presented in this paper serve to illustrate the effectiveness of the proposed method by acting on an inverted pendulum system in this paper, which can be showed that the computational burden is significantly reduced, and the predictive control performance of ETMPC is as well as the MPC roughly.