Joint friction has a significant influence on the dynamics and motion control of manipulators. However, the friction effect is often omitted or simplified in previous studies. In this paper, we establish the dynamics model of a single joint in a rotating industrial manipulator taking detailed friction effects into consideration and propose a new control algorithm for the friction compensation purpose. Firstly, the manipulator dynamics modeling is carried out employing a recently-proposed extended static friction model, which depicts load and temperature influence on Coulomb, Stribeck and viscous terms. Moreover, based on the established dynamics model, the paper presents a new adaptive fast nonsingular terminal sliding mode (AFNTSM) controller. The proposed approach has the advantages of continuous control inputs, fast convergence rate, no singularity and great robustness against disturbances. Furthermore, its adaptive property does not require any prior knowledge of the upper bound of the uncertainties. Finally, the proposed controller is applied to the manipulator joint trajectory tracking problem with varying friction subject to load and temperature changes. The numerical simulation verifies the effectiveness of our proposed method and its advantages over other controllers.
SpongMWHutchinsonSVidyasagarM, et al. Robot modeling and control. Vol.
3.
New York:
Wiley, 2006.
2.
CraigJJ.Introduction to robotics: mechanics and control. 3rd ed.Upper Saddle River, NJ:
Pearson/Prentice Hall, 2005.
3.
Gao L, Yuan J, Han Z, et al. A friction model with velocity, temperature and load torque effects for collaborative industrial robot joints. In: 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), Vancouver, BC, Canada, 24--28 September 2017, pp.3027--3032. IEEE.
4.
Bona B and Indri M. Friction compensation in robotics: an overview. In: Proceedings of the 44th IEEE conference on decision and control, Seville, Spain, Spain, 15--15 December 2005, pp.4360--4367. IEEE.
5.
SunYHChenTQiong WuC, et al.
Comparison of four friction models: feature prediction. J Comput Nonlinear Dyn2016;
11: 031009.
6.
MarquesFFloresPPimenta ClaroJC, et al.
A survey and comparison of several friction force models for dynamic analysis of multibody mechanical systems. Nonlinear Dyn2016;
86: 1407–1443.
7.
PennestrìERossiVSalviniP, et al.
Review and comparison of dry friction force models. Nonlinear Dyn2016;
83: 1785–1801.
8.
JungJLeeDKimJS, et al.
Adaptive robust disturbance compensating control for a servo system in the presence of both friction and deadzone. Proc IMechE, Part C: J Mechanical Engineering Science2017;
231: 432–445.
9.
TsaiYKChanKY.Investigation on the impact of nongeometric uncertainty in dynamic performance of serial and parallel robot manipulators. Proc IMechE, Part C: J Mechanical Engineering Science2019;
233: 3487–3511.
10.
WangB.Coupling dynamic modelling of a flexible manipulator driven by servo joint using experimental identification method. Proc IMechE, Part C: J Mechanical Engineering Science2019;
233: 3076–3084.
11.
ZhangYGuYLiuT, et al.
Dynamic behavior and parameter sensitivity of the free-floating base for space manipulator system considering joint flexibility and clearance. Proc IMechE, Part C: J Mechanical Engineering Science2019;
233: 895–910.
12.
Xiu W and Ma O. Relation between end-effector impedance and joint friction of statically-balanced mechanisms. In: Proceedings of the ASME 2013 Dynamic Systems and Control Conference, Palo Alto, California, USA, 21--23 October 2013, vol. 56147, p. V003T48A006. ASME.
13.
Bittencourt AC, Wernholt E, Sander-Tavallaey S, et al. An extended friction model to capture load and temperature effects in robot joints. In: 2010 IEEE/RSJ international conference on intelligent robots and systems, Taipei, Taiwan, 18--22 October 2010, pp.6161--6167. IEEE.
14.
BittencourtACGunnarssonS.Static friction in a robot joint–modeling and identification of load and temperature effects. J Dyn Syst Measure Control2012;
134: 051013.
15.
Carvalho Bittencourt A, Axelsson P. Modeling and experiment design for identification of wear in a robot joint under load and temperature uncertainties based on constant-speed friction data [Internet]. Linköping; 2013. (LiTH-ISY-R). Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-90023
16.
BittencourtACAxelssonP.Modeling and experiment design for identification of wear in a robot joint under load and temperature uncertainties based on friction data. IEEE/ASME Trans Mechatron2014;
19: 1694–1706.
17.
Hamon P, Gautier M and Garrec P. Dynamic identification of robots with a dry friction model depending on load and velocity. In: 2010 IEEE/RSJ international conference on intelligent robots and systems, Taipei, Taiwan, 18--22 October 2010, pp.6187--6193. IEEE.
18.
Kammerer N and Garrec P. Dry friction modeling in dynamic identification for robot manipulators: theory and experiments. In: 2013 IEEE International Conference on Mechatronics (ICM), Vicenza, Italy, 27 February--1 March 2013, pp.422--429. IEEE.
19.
Carlson FB, Robertsson A and Johansson R. Modeling and identification of position and temperature dependent friction phenomena without temperature sensing. In: 2015 IEEE/RSJ international conference on intelli- gent robots and systems (IROS), Hamburg, Germany, 28 September--2 October 2015, pp. 3045--3051. IEEE.
20.
Simoni L, Beschi M, Legnani G, et al. Friction modeling with temperature effects for industrial robot manipulators. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), Hamburg, Germany, 28 September--2 October 2015, pp. 3524--3529. IEEE.
21.
SimoniLBeschiMLegnaniG, et al.
On the inclusion of temperature in the friction model of industrial robots. IFAC-PapersOnLine2017;
50: 3482– 3487.
22.
SimoniLBeschiMLegnaniG, et al.
Modelling the temperature in joint friction of industrial manipulators. Robotica2019;
37: 906–927.
23.
Legnani G, Simoni L, Beschi M, et al. A new friction model for mechanical transmissions considering joint temperature (Volume 6). In: Proceedings of the ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Charlotte, North Carolina, USA, 21--24 August 2016, vol. 50183, p. V006T09A020. ASME.
24.
Simoni L, Villagrossi E, Beschi M, et al. On the use of a temperature based friction model for a virtual force sensor in industrial robot manipulators. In: 2017 22nd IEEE international conference on emerging technologies and factory automation (ETFA), Limassol, Cyprus, 12--15 September 2017, pp. 1--6. IEEE.
25.
FengYYuXManZ.Non-singular terminal sliding mode control of rigid manipulators. Automatica2002;
38: 2159–2167.
26.
YuSYuXShirinzadehB, et al.
Continuous finite-time control for robotic manipulators with terminal sliding mode. Automatica2005;
41: 1957–1964.
27.
HeSLinDWangJ.Chattering-free adaptive fast convergent terminal sliding mode controllers for position tracking of robotic manipulators. Proc IMechE, Part C: J Mechanical Engineering Science2016;
230: 514–526.
28.
LiPMaJZhengZ.Robust adaptive sliding mode control for uncertain nonlinear mimo system with guaranteed steady state tracking error bounds. J Franklin Instit2016;
353: 303–321.
29.
BoukattayaMMezghaniNDamakT.Adaptive nonsingular fast terminal sliding-mode control for the tracking problem of uncertain dynamical systems. ISA Trans2018;
77: 1–19.
30.
YangXLiuHXiaoJ, et al.
Continuous friction feedforward sliding mode controller for a trimule hybrid robot. IEEE/ASME Trans Mechatron2018;
23: 1673–1683.
31.
ZhengJWangHManZ, et al.
Robust motion control of a linear motor positioner using fast nonsingular terminal sliding mode. IEEE/ASME Trans Mechatron2015;
20: 1743–1752.
32.
DuHChenXWenG, et al.
Discrete-time fast terminal sliding mode control for permanent magnet linear motor. IEEE Trans Ind Electron2018;
65: 9916–9927.
33.
SunZZhengJWangH, et al.
Adaptive fast non-singular terminal sliding mode control for a vehicle steer-by-wire system. IET Control Theory Appl2017;
11: 1245–1254.
34.
XuB.Composite learning finite-time control with application to quadrotors. IEEE Trans Syst Man Cybern, Syst2018;
48: 1806–1815.
35.
WangHLiZJinX, et al.
Adaptive integral terminal sliding mode control for automobile electronic throttle via an uncertainty observer and experimental validation. IEEE Trans Veh Technol2018;
67: 8129–8143.
36.
WangYYanFTianB, et al.
Nonsingular terminal sliding mode control of underwater remotely operated vehicles. Trans Can Soc Mech Eng2018;
42: 105–115.
TaheriEFerdowsiMHDaneshM.Design boundary layer thickness and switching gain in SMC algorithm for AUV motion control. Robotica2019;
37: 1785–1803.
43.
IoannouPAKokotovicPV.Instability analysis and improvement of robustness of adaptive control. Automatica1984;
20: 583–594.
44.
LevantA.Higher-order sliding modes, differentiation and output-feedback control. Int J Control2003;
76: 924–941.
45.
VisioliALegnaniG.On the trajectory tracking control of industrial scara robot manipulators. IEEE Trans Ind Electron2002;
49: 224–232.
46.
VillagrossiESimoniLBeschiM, et al.
A virtual force sensor for interaction tasks with conventional industrial robots. Mechatronics2018;
50: 78–86.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
