title: Cascade control of superheated steam temperature with neuro-PID controller.
abstract: In this paper, an improved cascade control methodology for superheated processes is developed, in which the primary PID controller is implemented by neural networks trained by minimizing error entropy criterion. The entropy of the tracking error can be estimated recursively by utilizing receding horizon window technique. The measurable disturbances in superheated processes are input to the neuro-PID controller besides the sequences of tracking error in outer loop control system, hence, feedback control is combined with feedforward control in the proposed neuro-PID controller. The convergent condition of the neural networks is analyzed. The implementation procedures of the proposed cascade control approach are summarized. Compared with the neuro-PID controller using minimizing squared error criterion, the proposed neuro-PID controller using minimizing error entropy criterion may decrease fluctuations of the superheated steam temperature. A simulation example shows the advantages of the proposed method.
authors: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China. zjhncepu@163.com
authors: Jianhua
authors: J
authors: Zhang
authors: Fenfang
authors: F
authors: Zhang
authors: Mifeng
authors: M
authors: Ren
authors: Guolian
authors: G
authors: Hou
authors: Fang
authors: F
authors: Fang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D006361:Heating / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D013227:Steam; D013696:Temperature
chemical_list: D013227:Steam