ParNMPC – a parallel optimisation toolkit for real-time nonlinear model predictive control
ParNMPC – a parallel optimisation toolkit for real-time nonlinear model predictive control
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ParNMPC – 用于实时非线性模型预测控制的并行优化工具包
DOI：
10.1080/00207179.2020.1798019
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发表时间：
2020
影响因子：
2.1
通讯作者：
T. Ohtsuka
中科院分区：
文献类型：
--
作者：
Haoyang Deng;T. Ohtsuka
非线性模型预测控制(NMPC)的实时优化一直是一个具有挑战性的问题，尤其是对于快速采样和大规模应用。本文提出了一种高效的NMPC并行化方法--ParNMPC。介绍了ParNMPC的实现细节，包括适用于并行化的专用离散化方法、牛顿法和并行法搜索方向计算统一的框架、保证收敛的线搜索方法和内点方法的热启动策略。为了评估ParNMPC在不同配置下的性能，给出了三个实验，包括四旋翼的闭环仿真、实验室直升机的真实控制实例和机器人操作手的闭环仿真。这些实验表明了ParNMPC在串、并行方面的有效性和高效性。
Real-time optimisation for nonlinear model predictive control (NMPC) has always been challenging, especially for fast-sampling and large-scale applications. This paper presents an efficient implementation of a highly parallelisable method for NMPC, called ParNMPC. The implementation details of ParNMPC are introduced, including a dedicated discretisation method suitable for parallelisation, a framework that unifies search direction calculation done using Newton's method and the parallel method, line search methods for guaranteeing convergence, and a warm start strategy for the interior-point method. To assess the performance of ParNMPC under different configurations, three experiments including a closed-loop simulation of a quadrotor, a real-world control example of a laboratory helicopter and a closed-loop simulation of a robot manipulator are shown. These experiments show the effectiveness and efficiency of ParNMPC both in serial and parallel.
影响因子：
4.8
作者：
Yang Wang;Stephen P. Boyd
通讯作者：
Yang Wang;Stephen P. Boyd