Data-Driven Stochastic Optimal Iterative Learning Control for Nonlinear Non-Affine Systems With Measurement Data Loss

IEEE Access

Hao Liang¹  Debin Kong¹  Yumei Sun¹  Yunkai Lv²  Ronghu Chi² 

[1] Engineering Institute, Yantai Nanshan University, Yantai, China;Institute of Artificial Intelligence and Control, School of Automation and Electronics Engineering, Qingdao University of Science and Technology, Qingdao, China;

关键词: Data-driven control;    stochastic optimal ILC;    nonlinear network systems;    measurement data loss;   

DOI  :  10.1109/ACCESS.2019.2941514

来源: DOAJ

【 摘 要 】

In this paper, the measurement data loss is considered and two data-driven stochastic optimal iterative learning control (DDSOILC) methods are presented directly for nonlinear network systems. Specifically, an iterative dynamic linearization (IDL) is adopted to construct the linear incremental input output relationship of the repetitive nonlinear network system between two consecutive iterations. In the sequel, a lifted IDL is obtained by defining two super vectors of inputs and outputs over the entire finite time interval. Then, a lifted IDL-based DDSOILC scheme is proposed where the random data loss is described by a Bernoulli distribution of random variable. The results are extended by using a non-lifted IDL where the input-output relationship is described pointwisely. The learning gains of the proposed two methods are iteration-time-variant and can be iteratively estimated using real-time data. The proposed two methods do not depend on any explicit model. Moreover, the proposed non-lifted IDL-based DDSOILC can use more control information than the proposed lifted IDL-based one, and thus it can achieve a better control performance. Both theoretical analysis and simulations verify the efficiency and applicability of the two proposed methods.

【 授权许可】

Unknown