Iterative Learning Control Based on Sliding Mode Technique for Hybrid Force/Position Control of Robot Manipulators

In this paper, an iterative learning control (ILC) method based on sliding mode technique is proposed for hybrid force/position control of robot manipulators. Different from traditional ILC, the main purpose of the proposed ILC is to learn the dynamic parameters rather than the control signals. The sliding mode technique is applied to enhance the robustness of the proposed ILC method against external disturbances and noise. The switching gain of the sliding mode term is time-varying and learned by ILC such that the chattering is suppressed effectively compared to traditional sliding mode control (SMC). Simulation studies are performed on a two degrees of freedom planar parallel manipulator. Simulation results demonstrate that the proposed method can achieve higher force/position tracking performance than the traditional SMC and ILC.

[1]  John J. Craig,et al.  Hybrid position/force control of manipulators , 1981 .

[2]  Esmaeel Khanmirza,et al.  Hybrid force/position control of robotic arms manipulating in uncertain environments based on adaptive fuzzy sliding mode control , 2018, Appl. Soft Comput..

[3]  Kevin L. Moore,et al.  Iterative Learning Control: Brief Survey and Categorization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[4]  Mehdi Keshmiri,et al.  Neural-network-based robust hybrid force/position controller for a constrained robot manipulator with uncertainties , 2018, Trans. Inst. Meas. Control.

[5]  Han Ding,et al.  Hybrid active/passive force control strategy for grinding marks suppression and profile accuracy enhancement in robotic belt grinding of turbine blade , 2021, Robotics Comput. Integr. Manuf..

[6]  A.G. Alleyne,et al.  A survey of iterative learning control , 2006, IEEE Control Systems.

[7]  Chou-Ching K. Lin,et al.  A rehabilitation robot with force-position hybrid fuzzy controller: hybrid fuzzy control of rehabilitation robot , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  Jian-Xin Xu,et al.  A survey on iterative learning control for nonlinear systems , 2011, Int. J. Control.

[9]  Adaptive Passivity-based Hybrid Pose/Force Control for Uncertain Robots , 2020 .

[10]  Haruhisa Kawasaki,et al.  Decentralized adaptive coordinated control of multiple robot arms without using a force sensor , 2006, Autom..

[11]  Mohammad Danesh,et al.  Adaptive hybrid force/position control of robot manipulators using an adaptive force estimator in the presence of parametric uncertainty , 2015, Adv. Robotics.

[12]  Bo Dong,et al.  Hybrid position–force control for constrained reconfigurable manipulators based on adaptive neural network , 2015 .

[13]  Luige Vladareanu,et al.  Hybrid Force-Position Dynamic Control of the Robots Using Fuzzy Applications , 2012 .

[14]  Huafeng Ding,et al.  Data‐driven tuning of feedforward controller structured with infinite impulse response filter via iterative learning control , 2019, IET Control Theory & Applications.

[15]  Wenkang Xu.  Robotic Time-varying Force Tracking in Position-based Impedance Control , 2016 .

[16]  J. Horváth.  Locally convex spaces , 1973 .

[17]  Rajendra Prasad,et al.  Adaptive neuro fuzzy based hybrid force/position control for an industrial robot manipulator , 2016, J. Intell. Manuf..

[18]  Di Xiao,et al.  Sensor-based hybrid position/force control of a robot manipulator in an uncalibrated environment , 2000, IEEE Trans. Control. Syst. Technol..

[19]  Matthew T. Mason,et al.  Compliance and Force Control for Computer Controlled Manipulators , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[20]  Abílio Azenha.  Iterative learning in variable structure position/force hybrid control of manipulators , 2000, Robotica.

[21]  Carlos Perez-Vidal,et al.  Robust Hybrid Position-Force Control for Robotic Surface Polishing , 2018, Journal of Manufacturing Science and Engineering.

[22]  Kuang-Yow Lian,et al.  Sliding-mode motion/force control of constrained robots , 1998 .

[23]  Francesco Braghin,et al.  Iterative Learning Procedure With Reinforcement for High-Accuracy Force Tracking in Robotized Tasks , 2018, IEEE Transactions on Industrial Informatics.

[24]  Marina Valles,et al.  Hybrid force/position control for a 3-DOF 1T2R parallel robot: Implementation, simulations and experiments , 2016 .

[25]  Nagarajan Sukavanam,et al.  Neural network based hybrid force/position control for robot manipulators , 2011 .

[26]  David H. Owens,et al.  Newton method based iterative learning control for discrete non-linear systems , 2006 .

[27]  M. Moallem,et al.  Hybrid position/force control of a flexible parallel manipulator , 2011, J. Frankl. Inst..

[28]  D. H. Owens,et al.  Iterative learning control: quantifying the effect of output noise , 2011 .

[29]  Suguru Arimoto,et al.  Bettering operation of Robots by learning , 1984, J. Field Robotics.

[30]  Hui Sun,et al.  Neural network–based terminal sliding mode applied to position/force adaptive control for constrained robotic manipulators , 2018 .

[31]  J. Norberto Pires,et al.  Force control experiments for industrial applications: a test case using an industrial deburring example , 2007 .

[32]  Dao Minh Tuan,et al.  Adaptive Position/Force Control for Robot Manipulators Using Force and Velocity Observer , 2019, Journal of Electrical Engineering & Technology.

[33]  Hamed Navvabi,et al.  Hybrid position/force control of Stewart Manipulator using Extended Adaptive Fuzzy Sliding Mode Controller (E-AFSMC). , 2019, ISA transactions.