In this study, trajectory tracking control of six–degrees of freedom robotic manipulator has been performed using the proposed fractional-order adaptive integral sliding mode control scheme. The proposed method is first composed by fractional-order and adaptive integral sliding mode control to achieve the finite-time convergence, chattering-free control inputs, better tracking performance and robustness for the robotic manipulator. Furthermore, an adaptive method has been utilized to evaluate and compensate the uncertain and unknown dynamics of the system without relying on the prior knowledge of the upper bounds. To illustrate the efficiency of the proposed fractional-order adaptive integral sliding mode control method, the real-time experimental studies have been performed for the six–degrees of freedom industrial robot manipulator. The experimental outcomes strongly verified that the proposed controller gives quite well trajectory tracking response and faster convergence compared with the classical integral sliding mode control under the external payload.
ChanSPLiawHC. Generalized impedance control of robot for assembly tasks requiring compliant manipulation. IEEE Trans Ind Electr1996; 43(4): 453–461.
2.
ChenYXuWLiZet al. Safety-enhanced motion planning for flexible surgical manipulator using neural dynamics. IEEE Trans Contr Syst Tech2017; 25(5): 1711–1723.
3.
LeeW-YShihC-LLeeS-T. Force control and breakthrough detection of a bone-drilling system. IEEE/ASME Trans Mechatr2004; 9(1): 20–29.
4.
TakeiTImamuraRYutaS. Baggage transportation and navigation by a wheeled inverted pendulum mobile robot. IEEE Trans Ind Electr2009; 56(1): 3985–3994.
5.
MakarovMGrossardMRodríguez-AyerbePet al. Modeling and preview H∞ control design for motion control of elastic-joint robots with uncertainties. IEEE Trans Ind Electr2016; 63(1): 6429–6438.
6.
MbedeJBHuangXWangM. Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators. IEEE Trans Fuzzy Syst2003; 11(2): 249–261.
7.
NikdelNNikdelPBadamchizadehMAet al. Using neural network model predictive control for controlling shape memory alloy-based manipulator. IEEE Trans Ind Electr2014; 61(3): 1394–1401.
8.
BhattiJPlummerARSahinkayaMNet al. Fast and adaptive hopping height control of single-legged robot. In: Proceedings of the ASME 2012 11th biennial conference on engineering systems design and analysis, Nantes, 2–4 July 2012. ASME Digital Collection.
9.
DumluAErentürkKKaleliAet al. A comparative study of two model-based control techniques for the industrial manipulator. Robotica2017; 35(10): 2036–2055.
10.
ViolaJAngelL. Identification, control and robustness analysis of a robotic system using fractional control. IEEE Latin America Trans2015; 13(5): 1294–1302.
11.
MobayenS. A novel global sliding mode control based on exponential reaching law for a class of under-actuated systems with external disturbances. J Comput Nonlinear Dynam2015; 11(2): 11–21.
12.
ZakHHuiS. On variable structure output feedback controllers for uncertain dynamic systems. IEEE Trans Automat Contr1993; 38(10): 1509–1512.
13.
EdwardsCSpurgeonSK. Sliding mode stabilization of uncertain systems using only output information. Int J Contr2001; 65(5): 1129–1144.
14.
FridmanLM. An averaging approach to chattering. IEEE Trans Automat Contr2001; 46(8): 1260–1265.
15.
BartoliniGFerraraAUsaiE. Chattering avoidance by second-order sliding mode control. IEEE Trans Automat Contr1998; 43(2): 241–246.
16.
YuSYuXShirinzadehB. Continuous finite-time control for robotic manipulators with terminal sliding mode control. Automatica2005; 41(11): 1957–1964.
17.
ShtesselYFridmanLZinoberA. Higher order sliding modes. Int J Robust Nonlinear Contr2008; 18: 381–384.
18.
CaoW-JXuJ-X. Nonlinear integral-type sliding surface for both matched and unmatched uncertain systems. IEEE Trans Automat Contr2004; 49(8): 1355–1360.
19.
UtkinVShiJ. Integral sliding mode in systems operating under uncertainty conditions. In: Proceedings of 35th IEEE conference on decision and control, Vol. 4, Kobe, Japan, 13 December 1996, pp.4591–4596. New York: IEEE.
20.
MondalSMahantaC. Adaptive second order terminal sliding mode controller for robotic manipulators. J Franklin Inst2014; 351(4): 2356–2377.
21.
BaekJJinMHanS. A new adaptive sliding-mode control scheme for application to robot manipulators. IEEE Trans Ind Electr2016; 63(6): 3628–3637.
22.
VeerAShitalC. Adaptive robust control of Mecanum-wheeled mobile robot with uncertainties. Nonlinear Dyn2017; 87: 2147–2169.
23.
EfeMÖ. Fractional fuzzy adaptive sliding-mode control of a 2-DOF direct-drive robot arm. IEEE T Syst Man Cy B2008; 38(6): 1561–1570.
24.
WangYGuLXuYet al. Practical tracking control of robot manipulators with continuous fractional-order nonsingular terminal sliding mode. IEEE Trans Ind Electr2016; 63(10): 6194–6204.
25.
DasS. Functional fractional calculus for system identification and controls. London: Springer, 2008.
26.
ZeinaliMNotashL. Adaptive sliding mode control with uncertainty estimator for robot manipulators. Mech Mach Theory2010; 45(1): 80–90.
27.
EfeMÖ. Integral sliding mode control of a quadrotor with fractional order reaching dynamics. Trans Inst Measure Contr2010; 33: 985–1003.
28.
LevantA. Sliding order and sliding accuracy in sliding mode control. Int J Control1993; 58(6): 1247–1263.
MATLAB optimization toolbox release2016b. Natick, MA: The MathWorks Inc.
32.
HookeRJeevesTA. “Direct search” solution of numerical and statistical problems. J ACM1961; 8(2): 212–229.
33.
LewisRMTorczonVTrossetMW. Direct search methods: then and now. J Comput Appl Math2000; 124(1): 191–207.
34.
TepljakovAPetlenkovEBelikovJ. FOMCON: fractional-order modeling and control toolbox for MATLAB. In: Proceedings of the 18th international conference on mixed design of integrated circuits and systems, Gliwice, 16–18 June 2011, pp.684–689. New York: IEEE.
35.
ViveirosCMelícioRIgrejaJMet al. Performance assessment of a wind turbine using benchmark model: fuzzy controllers and discrete adaptive LQG. Procedia Technol2014; 17: 487–494.
36.
YongYSHuangYChibaRet al. Teaching-playback robot manipulator system in consideration of singularities. In: 2013 IEEE/ASME international conference on advanced intelligent mechatronics, Wollongong, NSW, Australia, 9–12 July 2013, pp.453–458. New York: IEEE.
