Efficient Convex Optimization for Linear MPC
Efficient Convex Optimization for Linear MPC
复制标题
线性 MPC 的高效凸优化
DOI：
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发表时间：
2018
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通讯作者：
Stephen J. Wright
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作者：
Stephen J. Wright
利用原始-对偶内点框架可以有效地求解具有线性动力学和二次目标的预测控制方程，其复杂性与视界长度成正比。另一种更能利用线性预测预测问题在每个决策点所解问题的相似性的方法是使用活动集方法，该方法将预测控制问题看作是由初始状态(x_{0})参数化的凸二次规划。另一种选择是显式地确定(x_{0})所占据的空间中的多面体区域，在该区域内有效约束集保持不变，并在每个区域上预先计算解算子。所有这些方法都在这里讨论。
MPC formulations with linear dynamics and quadratic objectives can be solved efficiently by using a primal-dual interior-point framework, with complexity proportional to the length of the horizon. An alternative, which is more able to exploit the similarity of the problems that are solved at each decision point of linear MPC, is to use an active-set approach, in which the MPC problem is viewed as a convex quadratic program that is parametrized by the initial state (x_{0}). Another alternative is to identify explicitly polyhedral regions of the space occupied by (x_{0}) within which the set of active constraints remains constant, and to pre-calculate solution operators on each of these regions. All these approaches are discussed here.