Open AccessDESIGN OF DYNAMIC NONLINEAR INTELLIGENT CONTROL SYSTEM FOR SIGNAL PROCESSING
Author(s) -
Zhiyou Wang,
Ying Chen,
Rayan A. Alsemmeari,
Yuan Zhou
Publication year - 2022
Publication title -
fractals
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 44
eISSN - 1793-6543
pISSN - 0218-348X
DOI - 10.1142/s0218348x22400916
Subject(s) - pid controller , nonlinear system , control theory (sociology) , artificial neural network , control system , computer science , control engineering , oscillation (cell signaling) , signal (programming language) , signal processing , intelligent control , control (management) , temperature control , artificial intelligence , engineering , digital signal processing , physics , quantum mechanics , computer hardware , programming language , electrical engineering , biology , genetics
Based on back propagation neural network (BPNN), we design a dynamic nonlinear intelligent control system for signal processing and analysis in this work. Performances of the traditional PID and BPNN control systems is compared in a case of heat power engineering system design. In the design, the training times, regulation time of the BPNN control system are evaluated when [Formula: see text] and [Formula: see text], respectively. Furthermore, the regulation time, oscillation time and amplitude of the traditional PID and BPNN control systems are compared in the dead zone. Results of the comparison show that the BPNN based dynamic nonlinear intelligent control system demonstrates superior performances over the traditional PID control system. The research results prove the advantage of BPNN in the design of nonlinear control systems and provide a scientific and effective reference for the follow-up research works.