Design and Simulation of Adaptive Internal Model Control Algorithm

• Published in: 2020 5th International Conference on Power and Renewable Energy (ICPRE) pp. 461 - 465
• Main Authors: Ming, Xie, Chunyu, Gao, Baojie, Liu, Ze, Dong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 12.09.2020
• Subjects: Adaptation models; Analytical models; Control systems; internal model control; large time delay; Mathematical models; parameter identification; particle swarm optimization; Robustness; Simulation; Software packages
• DOI: 10.1109/ICPRE51194.2020.9233106

In the actual industrial control, many objects have the characteristics of large delay and parameter time-varying, which makes the traditional PID control difficult to obtain satisfactory control effect. The internal model control has many advantages, such as intuitive design, strong practicability, simple structure, few on-line adjusting parameters and no need of accurate object model. Especially for the improvement of robustness and anti-interference and the control of the system with large time delay, the control effect is particularly significant. But the system model of internal model control is not easy to get, and it is difficult to get a more accurate internal model. In order to obtain a more accurate internal model, improve the control effect of the internal model control system and improve the dynamic characteristics of the control system, it is necessary to identify the model parameters of the controlled object efficiently. In this paper, the improved particle swarm optimization (PSO) algorithm is used to identify the system online and combined with the internal model control method to control the controlled object.