Compensation for secondary uncertainty in electro-hydraulic servo system by gain adaptive sliding mode variable structure control

Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system, adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty. At the same time, gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort. The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome. On the other hand, the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy, and the chattering phenomenon of the control effort is also suppressed effectively.