Continuous Reactor Temperature Control with Optimized PID Parameters Based on Improved Sparrow Algorithm

被引:11
作者
Ouyang, Mingsan [1 ]
Wang, Yipeng [1 ]
Wu, Fan [2 ]
Lin, Yi [1 ]
机构
[1] Anhui Univ Sci & Technol, Coll Elect & Informat Engn, Huainan 232000, Peoples R China
[2] Hangzhou Tianwa Network Technol Co Ltd, Hangzhou 310051, Peoples R China
基金
中国国家自然科学基金;
关键词
CSTR; improved sparrow search algorithm (ISSA); temperature control; golden sine; Gauss Cauchy mutation; SEARCH ALGORITHM;
D O I
10.3390/pr11051302
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
To address the problems of strong coupling and large hysteresis in the temperature control of a continuously stirred tank reactor (CSTR) process, an improved sparrow search algorithm (ISSA) is proposed to optimize the PID parameters. The improvement aims to solve the problems of population diversity reduction and easy-to-fall-into local optimal solutions when the traditional sparrow algorithm is close to the global optimum. This differs from other improved algorithms by adding a new Gauss Cauchy mutation strategy at the end of each iteration without increasing the time complexity of the algorithm. By introducing tent mapping in the sparrow algorithm to initialize the population, the population diversity and global search ability are improved; the golden partition coefficient is introduced in the explorer position update process to expand the search space and balance the relationship between search and exploitation; the Gauss Cauchy mutation strategy is used to enhance the ability of local minimum value search and jumping out of local optimum. Compared with the four existing classical algorithms, ISSA has improved the convergence speed, global search ability and the ability to jump out of local optimum. The proposed algorithm is combined with PID control to design an ISSA-PID temperature controller, which is simulated on a continuous reactor temperature model identified by modeling. The results show that the proposed method improves the transient and steady-state performance of the reactor temperature control with good control accuracy and robustness. Finally, the proposed algorithm is applied to a semi-physical experimental platform to verify its feasibility.
引用
收藏
页数:27
相关论文
共 28 条
[1]   An Advanced PID Based Control Technique With Adaptive Parameter Scheduling for A Nonlinear CSTR Plant [J].
Alshammari, Obaid ;
Mahyuddin, Muhammad Nasiruddin ;
Jerbi, Houssem .
IEEE ACCESS, 2019, 7 :158085-158094
[2]   Comparison of PID and FOPID controllers tuned by PSO and ABC algorithms for unstable and integrating systems with time delay [J].
Bingul, Zafer ;
Karahan, Oguzhan .
OPTIMAL CONTROL APPLICATIONS & METHODS, 2018, 39 (04) :1431-1450
[3]  
Chopra V, 2014, ACTA POLYTECH HUNG, V11, P235
[4]   Load Curtailment Optimization Using the PSO Algorithm for Enhancing the Reliability of Distribution Networks [J].
Cruz, Laura M. ;
Alvarez, David L. ;
Al-Sumaiti, Ameena S. ;
Rivera, Sergio .
ENERGIES, 2020, 13 (12)
[5]   Designing real time model mobile monitoring system for model predictive control in a nonlinear continuous stirred tank reactor [J].
Djarum, Danny Hartanto ;
Ahmad, Zainal .
ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, 2020, 15 (03)
[6]   Wavefront shaping using improved sparrow search algorithm to control the scattering light field [J].
Duan, Meigang ;
Yang, Zuogang ;
Zhao, Ying ;
Fang, Longjie ;
Zuo, Haoyi ;
Li, Zhensheng ;
Wang, Dequan .
OPTICS AND LASER TECHNOLOGY, 2022, 156
[7]   Adaptive fuzzy tuning of PID controllers [J].
Esfandyari, Morteza ;
Fanaei, Mohammad Ali ;
Zohreie, Hadi .
NEURAL COMPUTING & APPLICATIONS, 2013, 23 :S19-S28
[8]   Reduction of petroleum hydrocarbons content from an engine oil refinery wastewater using a continuous stirred tank reactor monitored by spectrometry tools [J].
Gargouri, Boutheina ;
Aloui, Fathi ;
Sayadi, Sami .
JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY, 2012, 87 (02) :238-243
[9]   A practical framework for implementing multivariate monitoring techniques into distributed control system [J].
Kazemi, Z. ;
Safavi, A. A. ;
Pouresmaeeli, S. ;
Naseri, F. .
CONTROL ENGINEERING PRACTICE, 2019, 82 :118-129
[10]   Chaotic grey wolf optimization algorithm for constrained optimization problems [J].
Kohli, Mehak ;
Arora, Sankalap .
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, 2018, 5 (04) :458-472