RESEARCH PAPER
Position/Force Control of Manipulator in Contact with Flexible Environment
1
Faculty of Mechanical Engineering and Aeronautics, Department of Applied Mechanics and Robotics, Rzeszow University of Technology, al. Powstańców Warszawy 12, 35-959 , Rzeszów, Poland
Submission date: 2018-06-26
Acceptance date: 2019-03-07
Online publication date: 2019-04-18
Publication date: 2019-03-01
Acta Mechanica et Automatica 2019;13(1):16-22
KEYWORDS
ABSTRACT
The paper presents the issue position/force control of a manipulator in contact with the flexible environment. It consists of the realisation of manipulator end-effector motion on the environment surface with the simultaneous appliance of desired pressure on the surface. The paper considers the case of a flexible environment when its deformation occurs under the pressure, which has a significant influence on the control purpose realisation. The article presents the model of the controlled system and the problem of tracking control with the use of neural networks. The control algorithm includes contact surface flexibility in order to improve control quality. The article presents the results of numerical simulations, which indicate the correctness of the applied control law.
REFERENCES (32)
1.
Barata J.C.A., Hussein M.S. (2012), The Moore–Penrose pseudoinverse: A tutorial review of the theory, Brazilian Journal of Physics, 42(1-2), 146–165.
2.
Birglen L., Schlicht T. (2018), A statistical review of industrial robotic grippers, Robotics and Computer-Integrated Manufacturing, 49, 88–97.
3.
Burghardt A., Kurc K., Szybicki D., Muszyńska M., Nawrocki J. (2017a), Software for the robot-operated inspection station for engine guide vanes taking into consideration the geometric variability of parts, Tehnicki Vjesnik-Technical Gazette, 24(2), 349–353.
4.
Burghardt A., Szybicki D., Kurc K., Muszyńska M., Mucha J. (2017b), Experimental Study of Inconel 718 Surface Treatment by Edge Robotic Deburring with Force Control, Strength Mater, 49(4), 594–604.
5.
Canudas de Wit C.A., Siciliano B., Bastin G. (Eds.) (1996), Theory of robot control, New York, Springer.
6.
Capisani L. M., Ferrara A. (2012), Trajectory planning and second-order sliding mode motion/interaction control for robot manipulators in unknown environments, IEEE Transactions on Industrial Electronics, 59(8), 3189–3198.
7.
Denkena B., Bergmann B., Lepper T. (2017), Design and optimization of a machining robot, Procedia Manufacturing, 14, 89–96.
8.
Duan J., Gan Y., Chen M., Dai X. (2018), Adaptive variable impedance control for dynamic contact force tracking in uncertain environment, Robotics and Autonomous Systems, 102, 54–65.
9.
Galushkin A. I. (2007). Neural networks theory, Springer Science & Business Media.
10.
Gierlak P. (2012), Hybrid Position/Force Control of the SCORBOT-ER 4pc Manipulator with Neural Compensation of Nonlinearities, in: Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L.A., Zurada J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science, 7268, 433–441, Springer, Berlin, Heidelberg.
11.
Gierlak P. (2014), Hybrid position/force control in robotised machining, Solid State Phenomena, 210, 192–199.
12.
Gierlak P. (2018), Combined strategy for control of interaction force between manipulator and flexible environment, Journal of Control Engineering and Applied Informatics, 20(2), 64–75.
13.
Gierlak P., Szuster M. (2017), Adaptive position/force control for robot manipulator in contact with a flexible environment, Robotics and Autonomous Systems, 95, 80–101.
14.
Gracia L., Solanes J.E., Muñoz-Benavent P., Miro J.V., Perez-Vidal C., Tornero J. (2018), Adaptive Sliding Mode Control for Robotic Surface Treatment Using Force Feedback, Mechatronics, 52, 102–118.
15.
Hashemi S.M., Gürcüoğlu U., Werner H. (2013), Interaction control of an industrial manipulator using LPV techniques, Mechatronics, 23(6), 689–699.
16.
Hendzel Z., Burghardt A., Gierlak P., Szuster M. (2014), Conventional and fuzzy force control in robotised machining, Solid State Phenomena, 210, 178–185.
17.
Hertz J., Krogh A., Palmer R.G. (1991), Introduction to the theory of neural computation, Boston, Addison-Wesley Longman Publishing Co.
18.
Iglesias I., Sebastián M.A., Are, J.E. (2015), Overview of the state of robotic machining: Current situation and future potential, Procedia engineering, 132, 911–917.
19.
Jafari A., Ryu J.H. (2016), Independent force and position control for cooperating manipulators handling an unknown object and interacting with an unknown environment, Journal of the Franklin Institute, 353(4), 857–875.
20.
Kumar N., Panwar V., Sukavanam N., Sharma S.P., Borm J.-H. (2011), Neural network based hybrid force/position control for robot manipulators, International Journal of Precision Engineering and Manufacturing, 12(3), 419–426.
21.
Lewis F.L., Liu K., Yesildirek A. (1995), Neural Net Robot Controller with Guaranteed Tracking Performance, IEEE Transactions on Neural Networks, 6(3), 701–715.
22.
Lotz M., Bruhm H., Czinki A. (2014), An new force control strategy improving the force control capabilities of standard industrial robots, Journal of Mechanics Engineering and Automation, Vol. 4, 276–283.
23.
Mendes N., Neto P. (2015), Indirect adaptive fuzzy control for industrial robots: a solution for contact applications, Expert Systems with Applications, 4 (22), 8929–8935.
24.
Narendra K., Annaswamy A.M. (1987), A new adaptive law for robust adaptation without persistent excitation, IEEE Transactions on Automatic Control, 32(2), 134–145.
25.
Pao Y.-H., Park G.-H., Sobajic D.J. (1994), Learning and generalization characteristics of the random vector functional-link net, Neurocomputing, 6(2), 163–180.
26.
Pliego-Jiménez J., Arteaga-Pérez M.A. (2015), Adaptive position/force control for robot manipulators in contact with a rigid surface with uncertain parameters, European Journal of Control, 22, 1–12.
27.
Polycarpou M.M., Ioannu P.A. (1991), Identification and control using neural network models: design and stability analysis, California, University of Southern California.
28.
Ravandi A. K., Khanmirza E., Daneshjou K. (2018), Hybrid force/position control of robotic arms manipulating in uncertain environments based on adaptive fuzzy sliding mode control. Applied Soft Computing, 70, 864–874.
29.
Tian F., Lv C., Li Z., Liu G. (2016), Modeling and control of robotic automatic polishing for curved surfaces, CIRP Journal of Manufacturing Science and Technology, 14, 55–64.
30.
Vukobratovič M., Ekalo Y., Rodič A. (2002), How to Apply Hybrid Position/Force Control to Robots Interacting with Dynamic Environment, In: Bianchi G., Guinot J.-C., Rzymkowski C. (Eds.) Romansy, 14, 249–258, Vienna.
31.
Zhu D., Luo S., Yang L., Chen W., Yan S., Ding H. (2015), On energetic assessment of cutting mechanisms in robot-assisted belt grinding of titanium alloys, Tribology International, 90, 55–59.
32.
Żylski W., Gierlak P. (2010), Verification of Multilayer Neural-Net Controller in Manipulator Tracking Control, Solid State Phenomena, 164, 99–104.
| eISSN: | 2300-5319 |
| ISSN: | 1898-4088 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
