Safety-Certified Optimal Formation Control for Nonlinear Multiagents via High-Order Control Barrier Function

This paper presents a safety-certified optimal for-mation control scheme for nonlinear multi-agents to realize de-sired formation configuration under safety constraints, guaran-teeing a compromise between safety-critical and energy-saving performances. Firstly, a self-learning optimal formation policy enables agents to achieve optimal formation configuration, wherein optimal performance is guaranteed via a computational-ly-efficient adaptive dynamic programming (ADP) framework. Furthermore, by revisiting real-time and historical information, a novel weight updating rule with fixed-time convergence is elabo-rated, such that rapid weight regulation is realized without de-pending on the initial choices. Secondly, a minimally-invasive safe control policy with high-order control barrier function con-straints is constructed in obstacles-clustered environments, wherein collision risk is excluded by ensuring the forward invari-ance of the safety set. It is strictly proved that closed-loop errors are uniformly ultimately bounded. Finally, extensive simulations are verified the values and superiorities of proposed method.