We propose a distributed data-based predictive control scheme to stabilize a network system described by linear dynamics. Agents cooperate to predict the future system evolution without knowledge of the dynamics, relying instead on learning a data-based representation from a single sample trajectory. We employ this representation to reformulate the finite-horizon Linear Quadratic Regulator problem as a network optimization with separable objective functions and locally expressible constraints. We show that the controller resulting from approximately solving this problem using a distributed optimization algorithm in a receding horizon manner is stabilizing. We validate our results through numerical simulations.

中文翻译：

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基于数据的线性网络系统的后退水平控制

我们提出了一种基于分布式数据的预测控制方案来稳定由线性动力学描述的网络系统。代理合作预测未来的系统演化，而无需了解动力学，而是依赖于从单个样本轨迹中学习基于数据的表示。我们使用这种表示将有限水平线性二次调节器问题重新表述为具有可分离目标函数和局部可表达约束的网络优化。我们表明，使用分布式优化算法以后退的方式近似解决这个问题所产生的控制器是稳定的。我们通过数值模拟验证了我们的结果。