Adaptive position/force control for robot manipulator in contact with a flexible environment

The subject of the article is the adaptive position and force control of a robotic manipulator in interaction with flexible environment. The aim of the study is to provide a solution that takes into account the essential aspects of operation of the manipulator with the environment and at the same time can be actually implemented. A manipulatorenvironment system model taking into account motion resistance and environment elasticity. The position and force control task has been defined considering the manipulator and environment models. Asymptotic stability of the control system has been demonstrated considering the adaptation of parameters of the manipulator and the environment. Practical stability of the system has been demonstrated in the case of interference with the guaranteed stability of the adaptation of parameters without requiring persistence of excitation. Numerical analysis and experimental study of the issue has been presented. An interaction of robot manipulator with flexible environment is considered.A force/position tracking controller is proposed.No information on robot parameters is required.Practical stability is guaranteed by the adaptive controller.System stability is proved by using Lyapunov stability theory.

[1]  Douglas R. Mitchell,et al.  Force and Control , 2017 .

[2]  Shahaboddin Shamshirband,et al.  An adaptive trajectory tracking control of four rotor hover vehicle using extended normalized radial basis function network , 2017 .

[3]  Shahaboddin Shamshirband,et al.  Extreme learning machine for prediction of heat load in district heating systems , 2016 .

[4]  Enzo Baccarelli,et al.  Q*: Energy and delay-efficient dynamic queue management in TCP/IP virtualized data centers , 2017, Comput. Commun..

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

[6]  Wang Lei,et al.  Fuzzy force control of constrained robot manipulators based on impedance model in an unknown environment , 2005 .

[7]  Joe Falco,et al.  Best Practices and Performance Metrics Using Force Control for Robotic Assembly , 2012 .

[8]  Carlos Canudas de Wit,et al.  An experimental study of adaptive force/position control algorithms for an industrial robot , 2000, IEEE Trans. Control. Syst. Technol..

[9]  Yuxiang Wu,et al.  Adaptive neural motion/force control of constrained robot manipulators by position measurement , 2011, 2011 Seventh International Conference on Natural Computation.

[10]  K. Narendra,et al.  A new adaptive law for robust adaptation without persistent excitation , 1987 .

[11]  J. Slotine,et al.  On the Adaptive Control of Robot Manipulators , 1987 .

[12]  Andrzej Burghardt,et al.  Conventional and Fuzzy Force Control in Robotised Machining , 2013 .

[13]  Carlos Canudas de Wit,et al.  Theory of Robot Control , 1996 .

[14]  Farid Ferguene,et al.  Dynamic External Force Feedback Loop Control of a Robot Manipulator Using a Neural Compensator—Application to the Trajectory Following in an Unknown Environment , 2009, Int. J. Appl. Math. Comput. Sci..

[15]  Piotr Gierlak Hybrid Position/Force Control in Robotised Machining , 2013 .

[16]  Piotr Gierlak Hybrid Position/Force Control of the SCORBOT-ER 4pc Manipulator with Neural Compensation of Nonlinearities , 2012, ICAISC.

[17]  Rong Xiong,et al.  Adaptive Torque and Position Control for a Legged Robot Based on a Series Elastic Actuator , 2016 .

[18]  Miomir Vukobratović,et al.  How to Apply Hybrid Position/Force Control to Robots Interacting with Dynamic Environment , 2002 .

[19]  Mamoru Minami,et al.  Constraint-combined force/position hybrid control method with Lyapunov stability , 2011, SICE Annual Conference 2011.

[20]  Marco A. Arteaga,et al.  Adaptive position/force control for robot manipulators in contact with a rigid surface with unknown parameters , 2015, 2015 European Control Conference (ECC).

[21]  Mohammad Farrokhi,et al.  Robust adaptive neuro-fuzzy controller for hybrid position/force control of robot manipulators in contact with unknown environment , 2006, J. Intell. Fuzzy Syst..

[22]  Enzo Baccarelli,et al.  Energy-Efficient Adaptive Resource Management for Real-Time Vehicular Cloud Services , 2019, IEEE Transactions on Cloud Computing.

[23]  D. E. Whitney,et al.  Historical Perspective and State of the Art in Robot Force Control , 1987 .

[24]  Dexiang Deng,et al.  Real-Time Fabric Defect Detection Using Accelerated Small-Scale Over-Completed Dictionary of Sparse Coding , 2016 .

[25]  Piotr Gierlak,et al.  Approximate Dynamic Programming in Tracking Control of a Robotic Manipulator , 2016 .

[26]  Markus,et al.  A New Force Control Strategy Improving the Force Control Capabilities of Standard Industrial Robots , 2014 .

[27]  Haruhisa Kawasaki,et al.  Bending moment-based force control of flexible arm under gravity , 2014 .

[28]  Frank L. Lewis,et al.  Neural net robot controller with guaranteed tracking performance , 1995, IEEE Trans. Neural Networks.

[29]  R. E. Eckmiller,et al.  Neural Velocity Force Control for Industrial Manipulators Contacting Rigid Surfaces , 1998 .

[30]  Fengjie Tian,et al.  Modeling and control of robotic automatic polishing for curved surfaces , 2016 .