Predictive-Triggered Model Predictive Control: Theory and Application

摘要

This article develops a predictive-triggered model predictive control (MPC) approach based on parallel computing for continuous-time nonlinear system requiring fast response. Unlike the existing MPC approach, the proposed method advances the moment of solving MPC optimal control problem (OCP) by predictive-triggered mechanism and parallel computation method. Such a scheme ensures that optimal control input can be obtained timely by the controlled system, thereby removing the implicit assumption of existing MPC approach that OCP can be solved instantaneously. Besides, the input reconstruction mechanism is implemented to improve resource utilization of predictive-triggered MPC. Furthermore, the feasibility and closed-loop stability of the proposed predictive-triggered MPC are demonstrated, respectively. Finally, the proposed algorithm is tested to verify its resource utilization and real-time performance through both simulation and robot platform experiment.