Model predictive control of nonlinear system based on adaptive fuzzy neural network

被引：0
|
作者：
Zhou H. [1 ]
Zhang Y. [1 ]
Bai X. [1 ]
Liu B. [1 ]
Zhao H. [1 ]
机构：
[1] Faculty of Automation, Huaiyin Institute of Technology, Huai'an, 223003, Jiangsu
来源：
Huagong Xuebao/CIESC Journal | 2020年 / 71卷 / 07期
关键词：
Adaptive learning rate; Dynamic modeling; Fuzzy neural network; Model predictive control; Nonlinear systems; Process control;
D O I：
10.11949/0438-1157.20191531
中图分类号：
学科分类号：
摘要：
Aiming at the control problem of nonlinear dynamic systems, a model predictive control (MPC) method based on adaptive fuzzy neural network (AFNN) was proposed. First, in the offline modeling phase, a rule automatic splitting technique is used to generate initial fuzzy rules, and an improved adaptive LM learning algorithm is adopted to optimize network parameters. Second, in the real-time control process, the network parameters of the AFNN are adjusted according to the error between the system output and the predicted output, so that providing an accurate prediction model for the MPC. Furthermore, the gradient descent optimization algorithm with adaptive learning rate is used to solve optimization problem and obtain the nonlinear control law online, which is applied to control the dynamic system. In addition, the convergence and stability analysis of the proposed AFNN-MPC are given to ensure its successful application in practical engineering. Finally, numerical simulation and two-CSTR process experiments are used to verify the effectiveness of AFNN-MPC algorithm. The results show that the proposed AFNN-MPC has superior control performance. © All Right Reserved.
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页码：3201 / 3212
页数：11
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