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Research on a Nonlinear Robust Adaptive Control Method of the Elbow Joint of a Seven-Function Hydraulic Manipulator Based on Double-Screw-Pair Transmission
Author(s) -
Gaosheng Luo,
Jiawang Chen,
Linyi Gu
Publication year - 2014
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2014/184968
Subject(s) - control theory (sociology) , nonlinear system , controller (irrigation) , lyapunov function , backstepping , trajectory , robust control , adaptive control , elbow , engineering , lyapunov stability , control system , computer science , control (management) , artificial intelligence , medicine , physics , electrical engineering , surgery , quantum mechanics , astronomy , agronomy , biology
A robust adaptive control method with full-state feedback is proposed based on the fact that the elbow joint of a seven-function hydraulic manipulator with double-screw-pair transmission features the following control characteristics: a strongly nonlinear hydraulic system, parameter uncertainties susceptible to temperature and pressure changes of the external environment, and unknown outer disturbances. Combined with the design method of the back-stepping controller, the asymptotic stability of the control system in the presence of disturbances from uncertain systematic parameters and unknown external disturbances was demonstrated using Lyapunov stability theory. Based on the elbow joint of the seven-function master-slave hydraulic manipulator for the 4500 m Deep-Sea Working System as the research subject, a comparative study was conducted using the control method presented in this paper for unknown external disturbances. Simulations and experiments of different unknown outer disturbances showed that (1) the proposed controller could robustly track the desired reference trajectory with satisfactory dynamic performance and steady accuracy and that (2) the modified parameter adaptive laws could also guarantee that the estimated parameters are bounded.

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