Barrier-Certified Model Predictive Cooperative Path Following Control of Connected Autonomous Surface Vehicles
Barrier-Certified Model Predictive Cooperative Path Following Control of Connected Autonomous Surface Vehicles
复制标题
互联自主地面车辆的障碍认证模型预测协同路径跟踪控制
DOI：
10.1109/tnse.2023.3260259
复制
发表时间：
2023-11
影响因子：
6.6
通讯作者：
Guanghao Lv;Zhouhua Peng;Yongming Li;Lu Liu;Dan Wang
中科院分区：
文献类型：
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作者：
Guanghao Lv;Zhouhua Peng;Yongming Li;Lu Liu;Dan Wang
本文研究了在静态和动态障碍物以及物理约束条件下，互联自主水面车辆的安全协作路径跟踪问题。提出了一种具有避碰能力和约束满足能力的障碍认证模型预测协同路径跟踪控制方法。具体而言，鲁棒精确微分器为基础的扩展状态观测器估计的未知动力学模型的不确定性和外部干扰。基于路径变量包容方法，设计了标称滚动时域控制律，实现了物理约束下的协同路径跟踪任务。基于控制障碍函数设计了安全最优控制律，使船舶在安全约束下产生最优的纵荡力和航向角。设计了一种滚动时域航向控制律来跟踪期望航向信号。约束二次规划问题的制定和解决通过神经动力学优化。仿真结果验证了所提出的障碍认证模型预测控制方法的有效性，合作路径跟踪受静态和动态障碍。
This paper investigates the safe cooperative path following of connected autonomous surface vehicles subject to static and dynamic obstacles, as well as physical constraints. A barrier-certified model predictive cooperative path following control method is proposed with the capability of collision avoidance and constraint satisfaction. Specifically, a robust-exact-differentiators-based extended state observer is employed to estimate the unknown kinetics including model uncertainties and external disturbances. Based on a path variable containment approach, a nominal receding-horizon control law is designed to achieve cooperative path following task within the physical constraints. A safe optimal control law is designed based on control barrier functions to generate optimal surge force and heading angle within the safety constraints. A receding-horizon heading control law is designed to track the desired heading signals. Constrained quadratic programming problems are formulated and solved via neurodynamic optimization. Simulation results are elaborated to validate the efficacy of the proposed barrier-certified model predictive control method for cooperative path following subject to static and dynamic obstacles.