Speed tracking control for hydraulic transformer system based on active regulating common pressure rail

作     者：Shen, Wei Shen, Chao Su, Xiaoyu 

作者机构：Univ Shanghai Sci & Technol Dept Mechatron Engn Shanghai 200093 Peoples R China Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310000 Peoples R China Shanghai Univ Engn Sci Coll Elect & Elect Engn Shanghai 201620 Peoples R China 

出 版 物：《IET CONTROL THEORY AND APPLICATIONS》 (IET控制论与应用)

年 卷 期：2020年第14卷第20期

页      面：3547-3556页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0804[工学-仪器科学与技术] 0811[工学-控制科学与工程] 

基　　金：National Natural Science Foundation of China Natural Science Foundation of Shanghai, China [19ZR1435400] Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems, China [GZKF-201708] 

主　　题：valves adaptive control Lyapunov methods function approximation nonlinear control systems neurocontrollers robust control closed loop systems delays control system synthesis tracking fuzzy neural nets virtual control signals compensation mechanism nonlinear characteristics pi-sigma fuzzy neural network function approximator finite-time command control technology robust controller hydraulic transformer system active regulating common pressure rail speed tracking control problem hydraulic transformer controlled system input delay Pade approximation approach hydraulic swing motor 

摘      要：This study investigates the speed tracking control problem for the new hydraulic transformer controlled system based on the active regulating common pressure rail with both input delay and strong interference. Firstly, the Pade approximation approach is adopted to approximate and compensate for the input delay of the valve plate rotating controlled by the hydraulic swing motor. Then, the Levant filter is introduced to solve the explosion of complexity caused by repeated differentiation of the virtual control signals, and the compensation mechanism is constructed to compensate for the filtering errors. Besides, due to the strong non-linear characteristics of the proposed system, the pi-sigma fuzzy neural network is employed as the function approximator to address the non-linear terms. Based on finite-time command control technology and the barrier Lyapunov functionals, a robust controller is synthesised to ensure a better error convergence performance while without violating the state constraint. Finally, the effectiveness of the controller is verified by comparison with the traditional control method and simulation result.