Parameter optimization of nonlinear PID controller using RBF neural network for continuous stirred tank reactor

The temperature system of the Continuous Stirred Tank Reactor (CSTR) has the characteristics of strong nonlinearity and uncertain parameters. The linear PID controller makes it difficult to meet CSTR’s control requirements. Nonlinear PID (NPID) can improve the control effect of nonlinear controlled...

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Bibliographic Details
Published in:Measurement and control (London) 2023-11, Vol.56 (9-10), p.1835-1843
Main Authors: Shi, Xingxi, Zhao, Hong, Fan, Zheng
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Continuously stirred tank reactors
Control theory
Controllers
Effectiveness
Machine learning
Neural networks
Nonlinear control
Nonlinearity
Optimization
Parameter uncertainty
Proportional integral derivative
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Summary:The temperature system of the Continuous Stirred Tank Reactor (CSTR) has the characteristics of strong nonlinearity and uncertain parameters. The linear PID controller makes it difficult to meet CSTR’s control requirements. Nonlinear PID (NPID) can improve the control effect of nonlinear controlled objects, but due to the influence of nonlinear function selection and manual parameter setting, when parameters are uncertain or subject to external interference, the control performance of the system will decrease. To improve the adaptive capability of the NPID controller, the RBF-NPID control algorithm is proposed. The learning ability of RBF neural network is used to adjust NPID parameters online to improve the control performance of the system. In order to verify the effectiveness of the proposed algorithm, a CSTR model was established in MATLAB and algorithm comparison research was carried out. Simulation results show the effectiveness and superiority of the proposed algorithm.
ISSN:0020-2940
2051-8730
DOI:10.1177/00202940231189307