Jun Xiao

Work place: Anhui TipWorld Electric Tech. Co., Ltd., Hefei, China

E-mail: x_iaoj_un@163.com

Website:

Research Interests: Computational Engineering, Engineering

Biography

Jun Xiao: was born on December 9, 1986 in AnHui, China. He received the B.Sc degree and the M.Sc degree from Hefei University of Technology, Hefei, China, in 2008 and 2011, respectively. Now his research interests include Power Quality, FPGA Application.

Author Articles
PID Controller Parameters Tuning Based-on Satisfaction for Superheated Steam Temperature of Power Station Boiler

By Benxian Xiao Jun Xiao Rongbao Chen Yanhong Li

DOI: https://doi.org/10.5815/ijitcs.2014.07.02, Pub. Date: 8 Jun. 2014

Proposed the PID controller parameters tuning method based-on New Luus-Jaakola (NLJ) algorithm and satisfaction idea. According to the different requirements of each performance index, designed the satisfaction function with fuzzy constraint attributes, and then determined the comprehensive satisfaction function for PID tuning by NLJ algorithm. Provided the steps of PID controller parameters tuning based on the NLJ algorithm and satisfaction, and applied this tuning method to the cascade control system of superheated steam temperature for Power Station Boiler. Finally the simulation and experiment results have shown the proposed method has good dynamic and static control performances for this complicated superheated steam temperature control system.

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Research of IGPC Control Strategy Based-on Hybrid Optimization for Power Station Boiler Superheated Steam Temperature

By Benxian Xiao Rongbao Chen Jun Xiao

DOI: https://doi.org/10.5815/ijitcs.2014.02.05, Pub. Date: 8 Jan. 2014

Implicit Generalized Predictive Control (IGPC) algorithm can directly identify controller parameters without the need of solving Diophantine equation, thus can reduce the on-line algorithm computation time. In order to improve IGPC performance and extend its application, modified Particle Swarm Optimization (PSO) algorithm is introduced into IGPC rolling horizon optimization, combined with general IGPC gradient optimization method under unconstrained condition, a new hybrid optimization method is obtained, this modified IGPC can be used to both of the non-constraint industry process control and the constraint industry process control. Aiming at the superheated steam temperature control of sub-critical 600MW boiler, a new cascade compound control strategy that combines an outer loop IGPC master adjuster and an inner loop PID auxiliary adjuster is adopted. Finally the simulation results have shown that the proposed method can constrain the control action, prevent dramatic change of the input signal, thus can achieve good static and dynamic performances.

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