DocumentCode :
3232937
Title :
Identification and control of eltro-hydraulic servo system based on direct dynamic recurrent fuzzy neural network
Author :
Huang yuanfeng ; Zhang Youwang
Author_Institution :
Sch. of Electr. & Electron. Eng., Wuhan Inst. of Technol., Wuhan, China
fYear :
2009
fDate :
25-28 July 2009
Firstpage :
637
Lastpage :
642
Abstract :
For the affine nonlinear system having characteristics of differential relations between states, an adaptive dynamic recurrent fuzzy neural network (ADRFNN) taking only some measurable states as its inputs and describing the system´s inner dynamic relation by its feedback matrix was proposed to control the system, adaptive laws of the adjustable parameters and the evaluation errors´ bounds of ADRFNN were formulated based on Lyapunov stability theory, and stable direct ADRFNN controller (ADRFNNC) with gain adaptive VSC (GAVSC) for the estimation errors by ADRFNN and the load disturbance were synthesized. It can overcome the shortcoming of the structural expansion caused by larger number of inputs in traditional adaptive fuzzy neural networks (TAFNN) taking all states as its inputs. The results of its applications to electro-hydraulic position tracking system (EHPTS) show that it has an advantage over the TAFNN controller (TAFNNC) in steady characteristics of system. On the other hand, the proposed control algorithm can also make the chattering of the system´s control effort weaker and the system possess more strong robustness.
Keywords :
Lyapunov methods; adaptive control; control nonlinearities; electrohydraulic control equipment; feedback; fuzzy control; fuzzy neural nets; neurocontrollers; nonlinear control systems; recurrent neural nets; servomechanisms; variable structure systems; Lyapunov stability theory; adaptive dynamic recurrent fuzzy neural network; afflne nonlinear system; chattering; electro-hydraulic position tracking system; electro-hydraulic servo system; error estimation; feedback matrix; gain adaptive VSC; load disturbance; robustness; stable direct ADRFNN; structural expansion; traditional adaptive fuzzy neural network; Adaptive control; Adaptive systems; Control system synthesis; Control systems; Fuzzy control; Fuzzy neural networks; Nonlinear control systems; Nonlinear dynamical systems; Programmable control; Servomechanisms; ADRFNN; EHPTS; GAVSC; robustness; secondary uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science & Education, 2009. ICCSE '09. 4th International Conference on
Conference_Location :
Nanning
Print_ISBN :
978-1-4244-3520-3
Electronic_ISBN :
978-1-4244-3521-0
Type :
conf
DOI :
10.1109/ICCSE.2009.5228349
Filename :
5228349
Link To Document :
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