论文标题
关于迭代学习模型预测控制器的最佳和收敛属性
On the Optimality and Convergence Properties of the Iterative Learning Model Predictive Controller
论文作者
论文摘要
在此技术说明中，我们分析了线性确定性系统的学习模型预测控制（LMPC）策略的性能改进和最佳性能。 LMPC框架是一种策略迭代方案，其中使用闭环轨迹来更新控制策略以进行控制任务的下一次执行。我们表明，当线性独立性约束资格（LICQ）条件成立时，LMPC方案保证了严格的迭代性能改进和最佳性，这意味着在整个任务中评估的闭环成本均渐近地收敛于Infinite-Horizon-Horizon控制问题的最佳成本。与以前的工作相比，可以轻松地检查这种足够的LICQ条件，它适用于较大类别的系统，并且可以适应性地选择控制器的预测范围，如数字示例所示。
In this technical note we analyse the performance improvement and optimality properties of the Learning Model Predictive Control (LMPC) strategy for linear deterministic systems. The LMPC framework is a policy iteration scheme where closed-loop trajectories are used to update the control policy for the next execution of the control task. We show that, when a Linear Independence Constraint Qualification (LICQ) condition holds, the LMPC scheme guarantees strict iterative performance improvement and optimality, meaning that the closed-loop cost evaluated over the entire task converges asymptotically to the optimal cost of the infinite-horizon control problem. Compared to previous works this sufficient LICQ condition can be easily checked, it holds for a larger class of systems and it can be used to adaptively select the prediction horizon of the controller, as demonstrated by a numerical example.
