论文标题
使用稀疏扩展ADMM算法在嵌入式系统中跟踪模型预测控制
Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm
论文作者
论文摘要
本文介绍了用于实现模型预测控制（MPC）的稀疏，低内存足迹优化算法，用于在嵌入式系统中跟踪公式。该MPC公式比标准MPC公式具有多个优点，例如，即使在突然参考变化的情况下，吸引人的吸引力和保证递归可行性也会增加。但是，这是以牺牲MPC优化问题添加少量决策变量的代价，这使其矩阵的结构变得复杂。我们基于乘数的交替方向方法的扩展，提出了一种稀疏的优化算法，以利用该特定MPC公式的结构。我们描述了控制器公式，并详细介绍了如何通过上述优化算法利用其结构。我们显示了将提议的求解器与其他求解器和文献中的方法进行比较的闭环模拟。
This article presents a sparse, low-memory footprint optimization algorithm for the implementation of the model predictive control (MPC) for tracking formulation in embedded systems. This MPC formulation has several advantages over standard MPC formulations, such as an increased domain of attraction and guaranteed recursive feasibility even in the event of a sudden reference change. However, this comes at the expense of the addition of a small amount of decision variables to the MPC's optimization problem that complicates the structure of its matrices. We propose a sparse optimization algorithm, based on an extension of the alternating direction method of multipliers, that exploits the structure of this particular MPC formulation. We describe the controller formulation and detail how its structure is exploited by means of the aforementioned optimization algorithm. We show closed-loop simulations comparing the proposed solver against other solvers and approaches from the literature.
