Superheated Steam Temperature Cascade Control System Based on Fuzzy-Immune PID

Author(s):  
Keming Xie ◽  
Wei Hao ◽  
Jun Xie
Author(s):  
Zhijie Wang ◽  
Guangjun Wang ◽  
Hong Chen

Since the superheated steam temperature system of boiler in thermal power plant is characterized as time varying and nonlinear, it is hard to achieve a satisfactory performance by the conventional proportional-integral-derivative (PID) cascade control scheme. This paper presents a design method of adaptive PID cascade control system for superheated steam temperature based on inverse model: First, the inner loop and the outer process in the cascade control system are equivalent to a generalized plant. A simplified Takagi–Sugeno (STS) fuzzy model is adopted to identify the inverse model of the generalized plant. By choosing the appropriate structure and optimizing with constrains for the parameters of the inverse model, the organic combination of the PID primary controller with the inverse model is realized. To maintain the structure of the existing conventional PID cascade control system in power plant without change, in the control process, the parameters of the primary controller are adjusted on-line according to the identification result of the inverse model of the generalized plant; thus an adaptive PID cascade control system is formed, which matches with the characteristics of the controlled plant. Through the simulation experiments of controlling superheated steam temperature, it is illustrated that the proposed scheme has good adaptability and anti-interference ability.


2011 ◽  
Vol 383-390 ◽  
pp. 111-117 ◽  
Author(s):  
Li Jun Chen ◽  
Bo Sun ◽  
Jian Chao Diao ◽  
Li Li Zhao

Aiming at that superheated steam temperature system exists the large inertia and large time delay of the dynamic characteristics,and the converge speed of the conventional CMAC neural network is not fast enough to the real-time system, a credit assignment CMAC (CA-CMAC) neural network control is adopted in superheated steam temperature control system, which is proposed to speed up the learning process in CMAC. The simulation of the superheated steam temperature control system shows that CA-CMAC converges faster than the conventional CMAC. This result illustrates the effectiveness of this method.


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