Safety-Critical Model Predictive Control with Self-Triggered Mechanism for Obstacle Avoidance

摘要

This paper presents a self-triggered model predictive control (MPC) algorithm with robust discrete control barrier functions (DCBF) for obstacle avoidance and position regulation control of mobile robots with limited resources. The algorithm incorporates robust DCBF to deal with the system's safety with external bounded disturbances, and make it as a self-triggered condition to guarantee the safety. The stable condition and an adaptive prediction horizon strategy are also used to ensure the stability and reduce the usage of limited resources. The result of simulation proves that the proposed algorithm is effective.