Adaptive Neural Sliding Mode Control for 3-DOF Planar Parallel Manipulators

This paper proposes a combination between a neural network and an adaptive sliding mode control for trajectory tracking control of a 3-DOF planar parallel manipulator. It has a complicated dynamic model, including modelling uncertainties, frictional uncertainties and external disturbances. The proposed control algorithm is to use a PID sliding mode surface, an adaptive sliding mode controller with a neural network to overcome the drawback of the traditional sliding mode controllers, such as slow response rate with variation of uncertainties and external disturbances, chattering, and upper bound values of undefined dynamics which affects system performance, high wear of moving mechanical parts and high heat losses in power circuits. The radial basis function neural network is designed to compensate for uncertainties and external disturbances, which allows small switching gain. Hence, the chattering can be significantly reduced. In addition, an adaptive control law is used to adaptively converge small switching gains of the sliding mode controller as the neural network reduces model uncertainties. The effectiveness of the proposed control strategy is demonstrated by simulations which are conducted by using the combination of Sim-Mechanics and SolidWorks.

[1]  Qitao Huang,et al.  PD control with gravity compensation for hydraulic 6-DOF parallel manipulator☆ , 2010 .

[2]  E. LESTER SMITH,et al.  AND OTHERS , 2005 .

[3]  Fang-Xiang Wu,et al.  Nonlinear PD control for trajectory tracking with consideration of the design for control methodology , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[4]  Shen Yin,et al.  Tracking Control of Surface Ships With Disturbance and Uncertainties Rejection Capability , 2017, IEEE/ASME Transactions on Mechatronics.

[5]  J. Koenderink.  Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.

[6]  Roberto Brega,et al.  Application of a nonlinear adaptive controller to a 6 DOF parallel manipulator , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[7]  Ilyas Eker,et al.  Sliding mode control with PID sliding surface and experimental application to an electromechanical plant. , 2006, ISA transactions.

[8]  Zhang Jinhui,et al.  Disturbance observer based integral sliding mode control for systems with mismatched disturbances , 2015, 2015 34th Chinese Control Conference (CCC).

[9]  Frank Chongwoo Park,et al.  Parallel Robots , 2015, Encyclopedia of Systems and Control.

[10]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[11]  Hee-Jun Kang,et al.  A neural network–based synchronized computed torque controller for three degree-of-freedom planar parallel manipulators with uncertainties compensation , 2018 .

[12]  B. Heimann,et al.  Advanced Model-Based Control of a 6-DOF Hexapod Robot: A Case Study , 2010, IEEE/ASME Transactions on Mechatronics.

[13]  Hee-Jun Kang,et al.  An Adaptive Fuzzy Terminal Sliding Mode Control Methodology for Uncertain Nonlinear Second-Order Systems , 2018, ICIC.

[14]  Madan M. Gupta,et al.  An adaptive switching learning control method for trajectory tracking of robot manipulators , 2006 .

[15]  W. Marsden.  I and J , 2012 .

[16]  J. Merlet.  Jacobian, Manipulability, Condition Number and Accuracy of Parallel Robots , 2005, ISRR.

[17]  Vadim I. Utkin,et al.  Sliding Modes in Control and Optimization , 1992, Communications and Control Engineering Series.

[18]  Christopher Edwards,et al.  Sliding mode control : theory and applications , 1998 .

[19]  Halim Alwi,et al.  Design and Analysis of an Integral Sliding Mode Fault-Tolerant Control Scheme , 2012, IEEE Transactions on Automatic Control.

[20]  Shuang Cong,et al.  Nonlinear computed torque control for a high-speed planar parallel manipulator , 2009 .