论文标题
用于模型预测控制的计算控制的对数域内点方法
A Computationally Governed Log-domain Interior-point Method for Model Predictive Control
论文作者
论文摘要
本文介绍了一种解决模型预测控制（MPC）参考跟踪状态和控制约束的问题的计算高效方法。该方法由三个关键组成部分组成：首先，是构成整体方法基础的对数域内部二次编程方法；其次，一种通过使用上一个时间步中的MPC解决方案来暖启动此优化器的方法；第三，通过更改提供给MPC问题的参考命令来限制温暖启动的次级临时的计算调速器。结果，闭环系统以某种方式更改，以便可以使用每个时间步长较少的优化器迭代来计算MPC解决方案。在数值实验中，计算调速器将标准MPC实施的最差计算时间减少了90，同时保持良好的闭环性能。
This paper introduces a computationally efficient approach for solving Model Predictive Control (MPC) reference tracking problems with state and control constraints. The approach consists of three key components: First, a log-domain interior-point quadratic programming method that forms the basis of the overall approach; second, a method of warm-starting this optimizer by using the MPC solution from the previous timestep; and third, a computational governor that bounds the suboptimality of the warm-start by altering the reference command provided to the MPC problem. As a result, the closed-loop system is altered in a manner so that MPC solutions can be computed using fewer optimizer iterations per timestep. In a numerical experiment, the computational governor reduces the worst-case computation time of a standard MPC implementation by 90, while maintaining good closed-loop performance.
