论文标题
随机模型预测控制策略的稳定性和收敛性
Stability and Convergence of a Randomized Model Predictive Control Strategy
论文作者
论文摘要
RBM-MPC是模型预测控制（MPC）的计算有效变体，其中使用随机批处理方法（RBM）来加快每次迭代时有限的最佳控制问题。在本文中，对无约束线性系统的RBMMPC得出了稳定性和收敛估计。在数值示例中验证了所获得的估计值，该示例还显示了RBM-MPC的明确计算优势。
RBM-MPC is a computationally efficient variant of Model Predictive Control (MPC) in which the Random Batch Method (RBM) is used to speed up the finite-horizon optimal control problems at each iteration. In this paper, stability and convergence estimates are derived for RBMMPC of unconstrained linear systems. The obtained estimates are validated in a numerical example that also shows a clear computational advantage of RBM-MPC.
