A model free adaptive control method based on self-adjusting PID algorithm in pH neutralization process
|Updated:2026-01-06
|
A model free adaptive control method based on self-adjusting PID algorithm in pH neutralization process
Chinese Journal of Chemical EngineeringVol. 76, Issue 12, Pages: 227-236(2024)
Affiliations:
College of Information Science & Technology, Beijing University of Chemical Technology,Beijing,China,100029
Author bio:
Funds:
DOI:
CLC:
Published:2024
Accepted:
Scan QR Code
Kang Liu, You Fan, Juan Chen. A model free adaptive control method based on self-adjusting PID algorithm in pH neutralization process[J]. Chinese Journal of Chemical Engineering, 2024, 76(12): 227-236.
DOI:
Kang Liu, You Fan, Juan Chen. A model free adaptive control method based on self-adjusting PID algorithm in pH neutralization process[J]. Chinese Journal of Chemical Engineering, 2024, 76(12): 227-236.DOI:
A model free adaptive control method based on self-adjusting PID algorithm in pH neutralization process
a new model free adaptive control method based on self-adjusting PID algorithm (MFAC-SA-PID) is proposed to solve the problem that the pH process with strong nonlinearity is difficult to control near the neutralization point. The MFAC-SA-PID method also solves the problem that the parameters of the model free adaptive control (MFAC) method are not easy to be adjusted and the effect is not obvious by introducing a fuzzy self-adjusting algorithm to adjust the controller parameters. Then the convergence and stability of the MFAC-SA-PID method are proved in this paper. In the simulation study
the control performance of the MFAC-SA-PID method proposed in this paper is compared with the traditional MFAC method and the improved model free adaptive control (IMFAC) method
respectively. The results show that the proposed MFAC-SA-PID method has better control effect on the pH neutralization process. The MFAC-SA-PID control performance also outperforms the traditional MFAC method and IMFAC method when step input disturbances are added
which indicates that the MFAC-SA-PID method has better robustness and stability.
关键词
Keywords
references
The trial reading is over, you can activate your VIP account to continue reading.
Optimization of chemical looping hydrogen generation process via Aspen Plus—Machine learning integration
Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm
Refrigeration system synthesis based on de-redundant model by particle swarm optimization algorithm
A comparative process simulation study of Ca—Cu looping involving post-combustion CO2 capture
Optimal synthesis of compression refrigeration system using a novel MINLP approach
Related Author
Li Xinyu
Song Yiwen
Teng Shenglong
Zeng Dewang
Xu Jingxin
Danlei Chen
Yiqing Luo
Xigang Yuan
Related Institution
Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University
State Key Laboratory of Low-carbon Smart Coal-fired Power Generation and Ultra-clean Emission, China Energy Science and Technology Research Institute Co.,Ltd.
Chemical Engineering Research Center, School of Chemical Engineering and Technology, Tianjin University
State Key Laboratory of Chemical Engineering, Tianjin University
School of Chemical Engineering and Technology, Tianjin University