Adaptive Position/Force Control of a Robotic Manipulator in Contact with a Flexible and Uncertain Environment

The present paper concerns the synthesis of robot movement control systems in the cases of disturbances of natural position constraints, which are the result of surface susceptibility and inaccuracies in its description. The study contains the synthesis of control laws, in which the knowledge of parameters of the susceptible environment is not required, and which guarantee stability of the system in the case of an inaccurately described contact surface. The novelty of the presented solution is based on introducing an additional module to the control law in directions normal to the interaction surface, which allows for a fluent change of control strategy in the case of occurrence of distortions in the surface. An additional module in the control law is perceived as a virtual viscotic resistance force and resilient environment acting upon the robot. This interpretation facilitates intuitive selection of amplifications and allows for foreseeing the behavior of the system when disturbances occur. Introducing reactions of virtual constraints provides automatic adjustment of the robot interaction force with the susceptible environment, minimizing the impact of geometric inaccuracy of the environment.

[1]  Laurent Dubourg,et al.  Impact & improvement of tool deviation in friction stir welding , 2016 .

[2]  Carlos Perez-Vidal,et al.  Adaptive Sliding Mode Control for Robotic Surface Treatment Using Force Feedback , 2018, Mechatronics.

[3]  Dahu Zhu,et al.  On energetic assessment of cutting mechanisms in robot-assisted belt grinding of titanium alloys , 2015 .

[4]  Nuno Mendes,et al.  Indirect adaptive fuzzy control for industrial robots: A solution for contact applications , 2015, Expert Syst. Appl..

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

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

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

[8]  A. Burghardt,et al.  Experimental Study of Inconel 718 Surface Treatment by Edge Robotic Deburring with Force Control , 2017, Strength of Materials.

[9]  Piotr Gierlak Combined strategy for control of interaction force between manipulator and flexible environment , 2018 .

[10]  Piotr Gierlak,et al.  Adaptive position/force control for robot manipulator in contact with a flexible environment , 2017, Robotics Auton. Syst..

[11]  Dong Il Park,et al.  Assembly process monitoring algorithm using force data and deformation data , 2019, Robotics and Computer-Integrated Manufacturing.

[12]  Yahui Gan,et al.  Adaptive variable impedance control for dynamic contact force tracking in uncertain environment , 2018, Robotics Auton. Syst..

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

[14]  Nuno Mendes,et al.  Machines and control systems for friction stir welding: A review , 2016 .

[15]  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..

[16]  Antonella Ferrara,et al.  Trajectory Planning and Second-Order Sliding Mode Motion/Interaction Control for Robot Manipulators in Unknown Environments , 2012, IEEE Transactions on Industrial Electronics.

[17]  Berend Denkena,et al.  Design and optimization of a machining robot , 2017 .

[18]  Franz Dietrich,et al.  Application of artificial neural networks in force-controlled automated assembly of complex shaped deformable components , 2019, Procedia CIRP.

[19]  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..

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

[21]  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).

[22]  Miguel Ángel Sebastián,et al.  Overview of the State of Robotic Machining: Current Situation and Future Potential , 2015 .

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

[24]  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..

[25]  Piotr Gierlak Position/Force Control of Manipulator in Contact with Flexible Environment , 2019 .

[26]  Alexander Czinki,et al.  High performance force control - A new approach and suggested benchmark tests , 2015 .

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

[28]  Aghil Jafari,et al.  Independent force and position control for cooperating manipulators handling an unknown object and interacting with an unknown environment , 2016, J. Frankl. Inst..