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

This article presents a safety-certified optimal formation control scheme for nonlinear multiagents to realize desired formation configuration under safety constraints, guaranteeing a compromise between safety-critical and energy-saving performances. First, a self-learning optimal formation policy enables agents to achieve optimal formation configuration, wherein optimal performance is guaranteed via a computationally-efficient adaptive dynamic programming (ADP) framework. Furthermore, by revisiting real-time and historical information, a novel weight updating rule with fixed-time convergence is elaborated, such that rapid weight regulation is realized without depending on the initial choices. Second, a minimally-invasive safe control policy with high-order control barrier function constraints is constructed in obstacles-clustered environments, wherein collision risk is excluded by ensuring the forward invariance 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.