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.

中文翻译：

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使用分散学习模型预测控制的非线性多智能体系统的轨迹优化

我们提出了一种基于学习模型预测控制的分散式最小时间轨迹优化方案，用于具有非线性解耦动力学和耦合状态约束的多智能体系统。通过迭代执行相同的任务，来自先前任务执行的数据用于构建和改进本地时变安全集和近似值函数。这些在解耦 MPC 问题中用作终端集和终端成本函数。我们的框架产生了一个分散的控制器，它不需要在任务执行的每次迭代中代理之间的通信，并保证全局系统在任务迭代中的持久可行性、有限时间闭环收敛和不降低的性能。