文献类型：期刊文献

英文题名：Controller dynamic linearisation-based model-free adaptive control framework for a class of non-linear system

作者：Zhu, Yuanming[1];Hou, Zhongsheng[2]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Beijing Jiaotong Univ, Adv Control Syst Lab, Beijing, Peoples R China

年份：2015

卷号：9

期号：7

起止页码：1162

外文期刊名：IET CONTROL THEORY AND APPLICATIONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000353964100020)】；

基金：This work was supported by Major State Basic Research Development Program of China (2012CB720500), National Natural Science Foundation of China (61333010, 61203157), and Shanghai and Municipal Science and Technology Commission (15YF1402700, 13111103800) and Fundamental Research Funds for the Central Universities (22A201514051). Thanks Dr. Heqing Sun for her technical input.

语种：英文

摘要：Without the explicit process identification, the authors propose a model-free adaptive control framework for unknown plant by using the concept of equivalent dynamic linearisation controller. The controller has linear incremental structure and its local dynamics is equivalent to the ideal controller in theory. Hence, the problem of determining the structure of candidate controller is transformed to the problem of finding a sequence of local dynamic controllers to approximate the ideal controller. With the help of gradient information extracted from input and output (I/O) data of the plant, the optimal controller parameter sequence is generated by minimising a user-defined control criterion. This method gives a solution on how to determine the candidate controller structure. The controller design, parameter tuning and controller validation are based on I/O data of the plant. Hence, it could reduce the influence of internal disturbance or unmodelled dynamics. The effectiveness of the proposed method is illustrated by the simulation of a continuous polymerisation reaction process in a jacketed continuous stirred tank reactor system. Meanwhile, a simulation comparison is carried out to show the superiority of neural network data model in model-free adaptive control framework.