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Joint control for flexible‐joint robot with input‐estimation approach and LQG method
Author(s) -
Ji ChienYu,
Chen TsungChien,
Lee YungLung
Publication year - 2007
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.813
Subject(s) - linear quadratic gaussian control , control theory (sociology) , optimal projection equations , kalman filter , controller (irrigation) , torque , control system , control engineering , computer science , engineering , control (management) , artificial intelligence , physics , electrical engineering , agronomy , biology , thermodynamics
In this work, the input‐estimation (IE) algorithm and the linear quadratic Gaussian (LQG) controller are adopted to design a control system. The combined method can maintain higher control performance even when the system variation is unknown and under the influence of disturbance input. The IE algorithm is an on‐line inverse estimation method involving the Kalman filter (KF) and the least‐square method, which can estimate the system input without additional torque sensor, while the LQG control theory has the characteristic of low sensitivity of disturbance. The design and analysis processes of the controller will also be discussed in this paper. The joint control of the flexible‐joint robot system is utilized to test and verify the effectiveness of the control performance. According to the simulation results, the IE algorithm is an effective observer for estimating the disturbance torque input, and the LQG controller can effectively cope with the situation that the disturbance exists. Finally, higher control performance of the combined method for joint control of the robotic system can be further verified. Copyright © 2007 John Wiley & Sons, Ltd.