论文标题
具有状态和输入约束的线性系统的并行MPC
Parallel MPC for Linear Systems with State and Input Constraints
论文作者
论文摘要
本文提出了一种可行的可行算法，用于与状态和输入约束的线性二次模型预测控制（MPC）问题。该算法本身基于最初为具有输入约束的系统设计的并行MPC方案。在这种情况下，本文的一项贡献是构建时间变化但可分离的约束边缘，以确保在一般环境中递归的可行性和次级平行MPC的递归可行性和渐近稳定性，这也包括状态约束。此外，它显示了如何权衡在线运行时的保证与受加紧的状态限制引入的保守主义。在控制大规模的机器系统的背景下，分析了所提出的方法的相应性能以及递归可行性保证的成本。这是通过数值实验的大规模控制系统的数值实验来说明的，该系统具有100多个状态和60个控制输入，从而导致毫秒范围内的运行时间。
This paper proposes a parallelizable algorithm for linear-quadratic model predictive control (MPC) problems with state and input constraints. The algorithm itself is based on a parallel MPC scheme that has originally been designed for systems with input constraints. In this context, one contribution of this paper is the construction of time-varying yet separable constraint margins ensuring recursive feasibility and asymptotic stability of sub-optimal parallel MPC in a general setting, which also includes state constraints. Moreover, it is shown how to tradeoff online run-time guarantees versus the conservatism that is introduced by the tightened state constraints. The corresponding performance of the proposed method as well as the cost of the recursive feasibility guarantees is analyzed in the context of controlling a large-scale mechatronic system. This is illustrated by numerical experiments for a large-scale control system with more than 100 states and 60 control inputs leading to run-times in the millisecond range.
