论文标题
部分可观测时空混沌系统的无模型预测
Flock navigation with dynamic hierarchy and subjective weights using nonlinear MPC
论文作者
论文摘要
我们提出了一种基于模型预测控制（MPC）的方法，用于在领导者追随者框架中使用障碍物避免能力的羊群控制问题，利用每个代理商计算的未来轨迹预测。我们采用传统的雷诺的植入规则（凝聚力，分离和对齐方式）作为基础，并量身定制模型以适合导航（而不是形成）目的。特别是，我们介绍了几个概念，例如从邻居那里收集的信息的信誉和重要性，以及参考文献之间的动态权衡。它们基于以下观察结果，即近乎未来的预测更可靠，靠近领导者的代理是更多受过教育的信息的隐含载体，并且凝聚力或一致性的主导性是由代理商及其邻居之间的距离决定的。这些特征纳入了MPC公式中，并通过数值模拟讨论它们的优点。
We propose a model predictive control (MPC) based approach to a flock control problem with obstacle avoidance capability in a leader-follower framework, utilizing the future trajectory prediction computed by each agent. We employ the traditional Reynolds' flocking rules (cohesion, separation, and alignment) as a basis, and tailor the model to fit a navigation (as opposed to formation) purpose. In particular, we introduce several concepts such as the credibility and the importance of the gathered information from neighbors, and dynamic trade-offs between references. They are based on the observations that near-future predictions are more reliable, agents closer to leaders are implicit carriers of more educated information, and the predominance of either cohesion or alignment is dictated by the distance between the agent and its neighbors. These features are incorporated in the MPC formulation, and their advantages are discussed through numerical simulations.
