Fast Model Predictive Control Using Online Optimization
Fast Model Predictive Control Using Online Optimization
复制标题
DOI：
10.1109/tcst.2009.2017934
复制
发表时间：
2010-03
影响因子：
4.8
通讯作者：
Yang Wang;Stephen P. Boyd
中科院分区：
文献类型：
--
作者：
Yang Wang;Stephen P. Boyd
模型预测控制（MPC）的一个公认的缺点是，它通常只能用于具有缓慢动态的应用中，其中采样时间以秒或分钟为单位测量。一个众所周知的实现快速MPC的技术是离线计算整个控制律，在这种情况下，在线控制器可以实现为一个查找表。这种方法适用于状态和输入维数小（比如不超过5）、约束少和时间范围短的系统。在本文中，我们描述了一系列提高MPC速度的方法，使用在线优化。这些自定义方法利用MPC问题的特定结构，可以比使用通用优化器的方法快100倍地计算控制动作。作为一个例子，我们的方法计算的控制行动的问题，12个国家，3个控制，和地平线的30个时间步长（这需要解决一个二次规划450个变量和1284个约束）在5毫秒左右，允许MPC在200赫兹进行。
A widely recognized shortcoming of model predictive control (MPC) is that it can usually only be used in applications with slow dynamics, where the sample time is measured in seconds or minutes. A well-known technique for implementing fast MPC is to compute the entire control law offline, in which case the online controller can be implemented as a lookup table. This method works well for systems with small state and input dimensions (say, no more than five), few constraints, and short time horizons. In this paper, we describe a collection of methods for improving the speed of MPC, using online optimization. These custom methods, which exploit the particular structure of the MPC problem, can compute the control action on the order of 100 times faster than a method that uses a generic optimizer. As an example, our method computes the control actions for a problem with 12 states, 3 controls, and horizon of 30 time steps (which entails solving a quadratic program with 450 variables and 1284 constraints) in around 5 ms, allowing MPC to be carried out at 200 Hz.