Title: An improved single neuron self-adaptive PID control scheme of superheated steam temperature control system
Authors: Lei Yu; Jae Gyoung Lim; Shumin Fei
Addresses: School of Mechanical and Electric Engineering, Soochow University, Suzhou, JS512, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, SH21, China; Department of Railroad Electrical System Engineering, School of Railroad and Transportation, Woosong University, Daejeon, 300719, South Korea ' Department of Railroad Electrical System Engineering, School of Railroad and Transportation, Woosong University, Daejeon, 300719, South Korea ' Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, JS25, China
Abstract: The control unit of superheated steam temperature of thermal power plants has poor quality, and easy over-temperature problem. In this paper, according to the single neuron adaptive controller with self-learning, strong adaptability, high robustness and fast response, an improved single neuron self-adaptive proportional-integral-derivative (PID) control scheme of superheated steam temperature control system has been presented. The proposed control scheme has two main characteristics: 1) Compared with the traditional PID control scheme, the three PID control parameters of proportional, integral, differential coefficients become a neuron adaptive control coefficient K; 2) This proposed control strategy has provided a new theoretical basis and research methods in the application of control system of superheated steam temperature. Simulation results show that the control scheme has improved the control performance of large time delay, multi-disturbance, and has reflected the strong robustness, high stability and good control quality.
Keywords: superheated steam temperature; improved single neuron self-adaptive controller; PID control; large time delay system; robust control performance.
International Journal of System Control and Information Processing, 2017 Vol.2 No.1, pp.1 - 13
Received: 31 Jul 2015
Accepted: 07 Jan 2016
Published online: 18 May 2017 *