Adaptive Robust Control of Nonlinear Constrained Stirred-Tank Reactors via Self-Organizing Fuzzy Neural Network

IF 3.9 4区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-11-05 DOI:10.1002/acs.3929

Xiao-Song Cui, Dong-Juan Li, Dapeng Li

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Abstract

This paper presents an adaptive robust constraint controller for a continuous stirred tank reactor (CSTR) system based on a self-organizing fuzzy neural network (SOFNN). Due to the high complexity of the chemical reactions, the CSTR system contains many strong nonlinearities and uncertainties. This is the first time to introduce the SOFNN into the adaptive controller design of the CSTR system, which improves the adaptability to dynamic system changes through the adjustment of the fuzzy network structure. Meanwhile, the time-varying integral barrier Lyapunov functions (TVIBLFs) are employed to ensure the dimensionless reactant concentration and mixture temperature within a reasonable scope, which can improve the stability and safety of the CSTR system. Based on Lyapunov stability analysis, all the signals in a closed-loop system are ultimately bounded. Simulation results substantiate the efficacy of the proposed control scheme.

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基于自组织模糊神经网络的非线性约束搅拌槽反应器自适应鲁棒控制

提出了一种基于自组织模糊神经网络（SOFNN）的连续搅拌槽式反应器（CSTR）系统自适应鲁棒约束控制器。由于化学反应的高度复杂性，CSTR系统具有很强的非线性和不确定性。这是首次将SOFNN引入到CSTR系统的自适应控制器设计中，通过模糊网络结构的调整提高了对系统动态变化的适应性。同时，利用时变积分势垒Lyapunov函数（tviblf）保证了无量纲反应物浓度和混合物温度在合理范围内，提高了CSTR系统的稳定性和安全性。基于李雅普诺夫稳定性分析，闭环系统中的所有信号最终都是有界的。仿真结果验证了所提控制方案的有效性。

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来源期刊

International Journal of Adaptive Control and Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

16.10%

发文量

163

审稿时长

5 months

期刊介绍： The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.