Safety-Critical Receding-Horizon Planning and Formation Control of Autonomous Surface Vehicles via Collaborative Neurodynamic Optimization
提出一种三层编队控制架构，通过协同神经动力学优化生成安全编队轨迹，结合控制障碍函数避免碰撞，并设计抗干扰控制器，解决模型不确定性和环境扰动下的自主水面艇安全规划与编队控制问题。
This article addresses the safety-critical receding-horizon planning and formation control of autonomous surface vehicles (ASVs) in the presence of model uncertainties, environmental disturbances, as well as stationary and moving obstacles. A three-level formation control architecture is proposed with a safety-critical formation trajectory generation module at its high level, a collision-free guidance module at its middle level, and an anti-disturbance control module at its low level. Specifically, a safety-critical formation trajectory generator is designed by leveraging collaborative neurodynamic optimization to plan safe formation trajectories to track a given trajectory and avoid stationary obstacles in a receding-horizon manner. Based on control barrier functions, a collision-free line-of-sight guidance law is developed to generate safe guidance commands to avoid collision with moving obstacles and other vehicles. An anti-disturbance control law is customized with a finite-time convergent observer for a vehicle to follow the guidance command signals. Simulation and hardware-in-the-loop experimental results are elaborated to validate the efficacy of the proposed method for the receding-horizon planning and formation control of ASVs.