Decentralized robust receding horizon control for multi-vehicle guidance
Decentralized robust receding horizon control for multi-vehicle guidance
复制标题
用于多车辆引导的分散式鲁棒后退地平线控制
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
J. How
中科院分区：
文献类型：
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作者：
Y. Kuwata;Arthur G. Richards;Tom Schouwenaars;J. How
提出了一种多车辆轨迹优化的分散鲁棒模型预测控制算法。该算法是以前的鲁棒安全但知识渊博（RSBK）算法的扩展，该算法使用约束收紧技术来实现鲁棒性，不变集来确保安全性，以及成本去函数来生成围绕环境中障碍物的智能轨迹。虽然RSBK算法被证明比以前的鲁棒MPC算法更快地解决问题，但该方法基于集中计算，这对于大型车辆组是不切实际的。本文分散的算法，通过确保每个车辆总是有一个可行的解决方案的作用下的干扰。该算法的主要优点是，它只需要环境和其他车辆的本地知识，同时保证整个车队的鲁棒可行性。新方法还有利于分散式轨迹优化的更通用的实现架构，这进一步减少了由于计算时间引起的延迟
This paper presents a decentralized robust model predictive control algorithm for multi-vehicle trajectory optimization. The algorithm is an extension of a previous robust safe but knowledgeable (RSBK) algorithm that uses the constraint tightening technique to achieve robustness, an invariant set to ensure safety, and a cost-to-go function to generate an intelligent trajectory around obstacles in the environment. Although the RSBK algorithm was shown to solve faster than the previous robust MPC algorithms, the approach was based on a centralized calculation that is impractical for a large group of vehicles. This paper decentralizes the algorithm by ensuring that each vehicle always has a feasible solution under the action of disturbances. The key advantage of this algorithm is that it only requires local knowledge of the environment and the other vehicles while guaranteeing robust feasibility of the entire fleet. The new approach also facilitates a significantly more general implementation architecture for the decentralized trajectory optimization, which further decreases the delay due to computation time