解决分布式控制不一致问题，实现多智能体协同安全控制
Synchronization-Based Cooperative Distributed Model Predictive Control
- 通过同步预测状态确保各智能体决策一致性
- 在小型车辆集群实验中验证了算法有效性
- 适合需要安全约束的多机器人协同场景
分布式控制算法相比集中式算法能降低整体计算时间，但可能导致解决方案不一致，从而违反安全关键约束。当多个智能体同时进行控制动作预测时，这种不一致尤为明显。为此，我们提出一种名为基于同步的协作式分布式模型预测控制的迭代算法。该算法包含两个步骤：1）为每个智能体计算最优控制输入；2）在所有智能体间同步预测状态。我们在网络物理移动性实验室中对多辆小型车辆的控制任务验证了该算法的有效性。
原文摘要 · Abstract (English)
Distributed control algorithms are known to reduce overall computation time compared to centralized control algorithms. However, they can result in inconsistent solutions leading to the violation of safety-critical constraints. Inconsistent solutions can arise when two or more agents compute concurrently while making predictions on each others control actions. To address this issue, we propose an iterative algorithm called Synchronization-Based Cooperative Distributed Model Predictive Control, which we presented in [1]. The algorithm consists of two steps: 1. computing the optimal control inputs for each agent and 2. synchronizing the predicted states across all agents. We demonstrate the efficacy of our algorithm in the control of multiple small-scale vehicles in our Cyber-Physical Mobility Lab.
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