Dynamic modeling and neural network compensation for dual-flexible servo system with an underactuated hand

Abstract

This paper investigates a difficult problem of nonlinear dynamics and motion control of a dual-flexible servo system with an underactuated hand (DFSS-UH). Variation in grasping mass and nonlinear factors of the DFSS-UH including complex flexible deformation and friction torque aggravate the output speed fluctuation, leading to modeling errors in the dynamics, which in turn affects the underactuated hand motion accuracy. A novel neural network sliding mode control (NNSMC) method is designed to control the DFSS-UH. The strategy utilizes neural networks to compensate for dynamics modeling errors, which takes into account neglected nonlinear factors and inaccurate friction torque. The reaching law with the hyperbolic tangent function is proposed to improve sliding mode control, thereby weakening the chattering phenomenon. First of all, the DFSS-UH mechanical model considering many nonlinear factors is established and a dynamic simplification model which ignores higher-order modes is proposed. Secondly, the adaptive law of weighted coefficients is proposed according to the stability of the DFSS-UH. Finally, the physical control platform of the DFSS-UH is built, and simulation and control experiments are conducted. Experimental results show that the improved NNSMC strategy decreases the tracking error of flexible load, thereby enhancing the control accuracy of the DFSS-UH.