模型预测控制
控制理论(社会学)
非线性系统
人工神经网络
控制器(灌溉)
计算机科学
采样(信号处理)
收敛速度
理论(学习稳定性)
控制工程
工程类
控制(管理)
人工智能
机器学习
物理
滤波器(信号处理)
量子力学
农学
计算机视觉
生物
计算机网络
频道(广播)
作者
Honggui Han,Shijia Fu,Haoyuan Sun,Junfei Qiao
出处
期刊:IEEE Transactions on Automation Science and Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-07-01
卷期号:20 (3): 2182-2194
被引量:1
标识
DOI:10.1109/tase.2022.3197683
摘要
This paper is concerned with the asynchronous control problem of multi-rate sampled-data nonlinear systems. To solve this problem, the data-driven multi-model predictive control (DMMPC) strategy is proposed. First, a multi-model predictive control structure is designed such that each state variable of the multi-rate sampled-data nonlinear system can be controlled synchronously at all sampling instants. In this structure, the fuzzy neural network (FNN) is introduced to build the multi-model. Then, the prediction outputs of each state variable at all sampling instants are obtained to provide control information for the controller. Specially, the objective function with adaptive weight matrix (AWM) is designed to reduce the influence of the prediction error caused by nonlinear fitting on control performance. Then, the optimal control laws are calculated to improve the control precision. Finally, the convergence and stability of DMMPC are proved in detail. The numerical example and industrial application reveal that the proposed DMMPC can obtain considerable control performance for the multi-rate sampled-data nonlinear systems. Note to Practitioners —The asynchronous control problem of the multi-rate sampled-data nonlinear system (MRSNS) may degrade the operation performance of closed-loop system. In this paper, a data-driven multi-model predictive control (DMMPC) strategy is designed for MRSNS without mechanism model. The strategy mainly consists of three parts: First, a multi-model prediction structure based on fuzzy neural network (FNN) is established to obtain the prediction output of all variables at each sampling point. The parameters of FNNs are corrected at each sampling point to ensure prediction accuracy. Second, an objective function with an adaptive weight matrix (AWM) is designed to compute the control law, in which AWM is used to reduce the influence of prediction error on control performance. Third, the effectiveness of DMMPC is verified by a numerical example and an industrial application of the wastewater treatment process. The experimental results show that DMMPC can achieve satisfied operation performance in control accuracy.
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