Article

# Friction compensation for an industrial hydraulic robot

Dept. of Autom. Control, Los Andes Univ., Merida

IEEE control systems (Impact Factor: 2.37). 03/1999; DOI: 10.1109/37.745763 Source: IEEE Xplore

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**ABSTRACT:**To improve position tracking performance of servo systems, a position tracking control using adaptive back-stepping control(ABSC) scheme and recurrent fuzzy neural networks(RFNN) is proposed. An adaptive rule of the ABSC based on system dynamics and dynamic friction model is also suggested to compensate nonlinear dynamic friction characteristics. However, it is difficult to reduce the position tracking error of servo systems by using only the ABSC scheme because of the system uncertainties which cannot be exactly identified during the modeling of servo systems. Therefore, in order to overcome system uncertainties and then to improve position tracking performance of servo systems, the RFNN technique is additionally applied to the servo system. The feasibility of the proposed control scheme for a servo system is validated through experiments. Experimental results show that the servo system with ABS controller based on the dual friction observer and RFNN including the reconstruction error estimator can achieve desired tracking performance and robustness.03/2012; , ISBN: 978-953-51-0396-7 · 1.09 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**This paper presents a new compensation technique for dynamic friction. The proposed control utilizes a PD control structure and an adaptive estimation of the friction force based on an observer. Specifically, a nonlinear function is used to compensate the nonlinear effects of the parameters in the friction model. Simulations and experimental results verify the theory and show that the method can significantly improve the tracking performance of the motion control system.IEEE/ASME Transactions on Mechatronics 03/2011; · 3.14 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**In this paper, the interaction force between a surgical needle and soft tissue is studied. The force is modeled using a novel nonlinear dynamic model. Encouraged by the LuGre model for representing friction forces, the proposed model captures all stages of needle-tissue interaction, including puncture, cutting, and friction forces. An estimation algorithm for identifying the parameters of the model is presented. This online approach, which is based on sequential extended Kalman filtering, enables us to characterize the total contact force using an efficient mathematical model. The algorithm compares the axial force measured at the needle base with its expected value and then adapts the model parameters to represent the actual interaction force. While the nature of this problem is very complex, the use of multiple Kalman filters makes the system highly adaptable for capturing the force evolution during an interventional procedure in standard operating conditions. To evaluate the performance of our model, experiments were performed on artificial phantoms.IEEE Transactions on Instrumentation and Measurement 01/2012; 61(2):429-438. · 1.36 Impact Factor

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