Robust Predictive Control Algorithm Based on Parameter Variation Rate Information of Functional-Coefficient ARX Model
Robust Predictive Control Algorithm Based on Parameter Variation Rate Information of Functional-Coefficient ARX Model
复制标题
基于函数系数ARX模型参数变化率信息的鲁棒预测控制算法
DOI：
10.1109/access.2019.2901767
复制
发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Peng Xiaoyan
中科院分区：
文献类型：
--
作者：
Zhou Feng;Peng Hui;Zhang Ganglin;Zeng Xiaoyong;Peng Xiaoyan
考虑到多面体线性变参数（LPV）模型的保守性，该模型仅利用具有外生输入和径向基函数网络类型系数的状态依赖自回归模型（RBF-ARX模型）的上下界信息来构造。提出了一种基于RBF-ARX模型参数变化率信息的鲁棒预测控制算法。通过使用这些信息，用于包装系统的多面体LPV模型的凸多面体集的大小被大大压缩。从而大大提高了控制性能，降低了后续RPC算法的保守性。从RBF-ARX模型到多面体LPV状态空间模型的转换只需要利用RBF-ARX模型本身，导出的LPV模型是一种特殊的拟LPV自回归模型。因此，没有必要假设多面体LPV模型中的时变参数和/或参数变化率的界限必须是已知的或测量的。以一个广泛应用的连续搅拌釜式反应器过程控制为例，说明了该方法在利用RBF-ARX模型参数变化率信息改善阶跃响应控制性能和抗干扰性能方面的有效性。
Considering the conservativeness caused by the polytopic linear parameter varying (LPV) model, which is constructed using only the upper and lower bound information of the state-dependent auto-regressive model with eXogenous input and radial basis function network type coefficients (RBF-ARX model). In this paper, a robust predictive control (RPC) algorithm based on the parameter variation rate information of the RBF-ARX model is proposed. By using the information, the size of the convex polytopic sets used to wrap the system’s polytopic LPV model is compressed greatly. Thus, it improves greatly the control performance and reduces the conservativeness of the subsequent RPC algorithm. The conversion from the RBF-ARX model to the polytopic LPV state space model just uses the RBF-ARX model itself, and the derived LPV model is a special quasi-LPV autoregressive model. So, it is not necessary to assume that the time varying parameters and/or the bounds of the parameter variation rate in the polytopic LPV model must be known or measured. An example of a widely used continuous stirred-tank reactor process control is studied to illustrate the effectiveness of the proposed approach in terms of using parameter variation rate information of the RBF-ARX model to improve step response control performance and anti-jamming performance.