论文标题
使用分散学习模型预测控制对非线性多机构系统的轨迹优化
Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control
论文作者
论文摘要
我们提出了一个分散的最小时间轨迹优化方案，该方案基于具有非线性解耦动力学和耦合状态约束的多机构系统的学习模型预测控制。通过迭代执行相同的任务，将使用先前任务执行的数据来构建和改善本地时间变化的安全集和近似值函数。这些用于解耦MPC问题作为终端集和终端成本函数。我们的框架导致了一个分散的控制器，这不需要在任务执行的每次迭代中之间的代理之间的通信，并保证持续的可行性，有限的时间闭环收敛以及全球系统在任务迭代方面的不稳定性能。多车碰撞避免情况的数值实验证明了拟议方案的有效性。
We present a decentralized minimum-time trajectory optimization scheme based on learning model predictive control for multi-agent systems with nonlinear decoupled dynamics and coupled state constraints. By performing the same task iteratively, data from previous task executions is used to construct and improve local time-varying safe sets and an approximate value function. These are used in a decoupled MPC problem as terminal sets and terminal cost functions. Our framework results in a decentralized controller, which requires no communication between agents over each iteration of task execution, and guarantees persistent feasibility, finite-time closed-loop convergence, and non-decreasing performance of the global system over task iterations. Numerical experiments of a multi-vehicle collision avoidance scenario demonstrate the effectiveness of the proposed scheme.
