Joint buffetings suppression and trajectory tracking by fuzzy-based super-twist sliding mode control: Application to a fast parallel robot for PnP operations
Restricted accessResearch articleFirst published online November, 2024
Joint buffetings suppression and trajectory tracking by fuzzy-based super-twist sliding mode control: Application to a fast parallel robot for PnP operations
This paper deals with the suppression of robotic joint buffetings for enhanced precision of trajectory tracking by means of fuzzy-based super-twist sliding mode control, which is illustrated with a fast pick-and-place parallel robot for material handling. Prior to control design, the joint motions and torques are measure to identify the dynamic parameters of the robot with recursive least square method, which is experimentally validated for the further model-based control design. In order to suppress the joint buffetings and to improve the tracking accuracy, the control law by integrating fuzzy algorithm and second-order sliding mode control is designed, where the control performance is evaluated by observing the joint dynamics, compared to the classical computed torque control and fuzzy sliding mode variable structure control. The experimental results and comparative study show the effectiveness of the developed control scheme, regarding the joint buffetings suppression and trajectory tracking precision. The main contribution lies in the integrated fuzzy algorithm and second-order sliding mode control for the model-based control design, with acceptable computational cost and trajectory tracking accuracy, from the perspective of actual engineering application.
PierrotFCompanyO. H4: a new family of 4-dof parallel robots. In: 1999 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Cat. No. 99TH8399), 508–513, Atlanta, GA, USA, 19-23 September 1999. IEEE.
2.
KrutSCompanyONabatV, et al. Heli4: a parallel robot for SCARA motions with a very compact traveling plate and a symmetrical design. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 1656–1661, Beijing, China, 9-15 October 2006.
3.
AltuzarraOŞandruBPintoC, et al. A symmetric parallel Schönflies-motion manipulator for pick-and-place operations. Robotica2011; 29(6): 853–862.
4.
XieFLiuXJ. Design and development of a high-speed and high-rotation robot with four identical arms and a single platform. ASME J Mech Robot2015; 7(4): 041015.
5.
WuGLinZZhaoW, et al. A four-limb parallel Schönflies motion generator with full-circle end-effector rotation. Mech Mach Theory2020; 146: 103711.
6.
PierrotFReynaudCFournierA. DELTA: a simple and efficient parallel robot. Robotica1990; 8(2): 105–109.
7.
PierrotFNabatVCompanyO, et al. Optimal design of a 4-DOF parallel manipulator: from academia to industry. IEEE Trans Robot2009; 25(2): 213–224.
8.
BelzileBEskandaryPKAngelesJ. Workspace determination and feedback control of a pick-and-place parallel robot: analysis and experiments. IEEE Robot Autom Lett2020; 5(1): 40–47.
9.
GosselinCIsakssonMMarlowK, et al. Workspace and sensitivity analysis of a novel nonredundant parallel SCARA robot featuring infinite tool rotation. IEEE Robot Autom Lett2016; 1(2): 776–783.
10.
GauthierJFAngelesJNoklebyS. Optimization of a test trajectory for SCARA systems. In: LenarčičJPhilippeW (eds) Advances in Robot Kinematics: analysis and Design. Netherlands, Dordrecht: Springer, 2008, pp.225–234.
11.
WuGBaiSCaroS. A transmission quality index for a class of four-limb parallel schönflies motion generators. ASME J Mech Robot2018; 10(5): 051014.
12.
WuGBaiSHjørnetP. Architecture optimization of a parallel Schönflies-motion robot for pick-and-place applications in a predefined workspace. Mech Mach Theory2016; 106: 148–165.
13.
LiQWuFX. Control performance improvement of a parallel robot via the design for control approach. Mechatronics2004; 14(8): 947–964.
14.
WuJWangJYouZ. An overview of dynamic parameter identification of robots. Robot ComputIntegr Manuf2010; 26(5): 414–419.
15.
KhoslaPKKanadeT. Parameter identification of robot dynamics. 1985 24th IEEE conference on decision and control, 1754–1760, Fort Lauderdale, FL, USA, 1985. IEEE.
16.
BenimeliFMataVValeroF. A comparison between direct and indirect dynamic parameter identification methods in industrial robots. Robotica2006; 24(5): 579–590.
17.
RadkhahKKulicDCroftE. Dynamic parameter identification for the CRS A460 robot. In: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 3842–3847, San Diego, California, USA, Oct. 29-Nov. 22007. IEEE.
18.
WuWZhuSWangX, et al. Closed-loop dynamic parameter identification of robot manipulators using modified Fourier series. Int J Adv Robot Syst2012; 9(1): 29.
19.
JinJGansN. Parameter identification for industrial robots with a fast and robust trajectory design approach. Robot Comput Manuf2015; 31: 21–29.
20.
KovincicNMüllerAGattringerH, et al. Proc. ARW & OAGM Workshop, 44–53, Steyr, Austria, May 9-10 2019.
21.
Díaz-RodríguezMIriarteXMataV, et al. On the experiment design for direct dynamic parameter identification of parallel robots. Adv Robot2009; 23(3): 329–348.
22.
BriotSKrutSGautierM. Dynamic parameter identification of overactuated parallel robots. ASME J. Dyn. Syst. Mea. Control2015; 137(11): 111002.
23.
JiangZ-HIshidaTSunawadaM. Neural network aided dynamic parameter identification of robot manipulators 2006 IEEE international conference on systems, man and cybernetics, vol. 4, 3298–3303, Taipei, Taiwan, 2006. IEEE.
24.
GautierMPoignetP. Extended Kalman filtering and weighted least squares dynamic identification of robot. Control Eng Pract2001; 9(12): 1361–1372.
25.
SweversJVerdonckWNaumerB, et al. An experimental robot load identification method for industrial application. Int J Rob Res2002; 21(8): 701–712.
26.
GhannadiBSharif RazavianRMcPheeJ. A modified homotopy optimization for parameter identification in dynamic systems with backlash discontinuity. Nonlinear Dyn2019; 95(1): 57–72.
27.
ZhenSPengXLiuX, et al. A new PD based robust control method for the robot joint module. Mech Syst Signal Process2021; 161: 107958.
28.
ShrivastavaADallaVKAbhishekK. Optimum joint and trajectory tracking control of multi-axes space manipulator based on amnesia feedback controller. Proc IMechE, Part C: J Mechanical Engineering Science2023; 237(19): 4546–4563.
29.
StepanenkoYDostSDostS. Hybrid integrator backstepping control of robotic manipulators driven by brushless DC motors. IEEE/ASME Trans Mechatron1996; 1(4): 266–277.
30.
AbouelsoudAA. Robust regulator for flexible-joint robots using integrator backstepping. J Intell Robot Syst1998; 22(1): 23–38.
31.
LotfazarAEghtesadMMohseniM. Integrator backstepping control of a 5 DoF robot manipulator incorporating actuator dynamics. In: Proceedings of 2003 IEEE Conference on Control Applications, vol. 2, 1007–1012. IEEE, 2003.
32.
LiYLiuYLiuX. Active vibration control of a modular robot combining a back-propagation neural network with a genetic algorithm. J Vib Control2005; 11(1): 3–17.
33.
VázquezLAJuradoFCastañedaCE, et al. Real-time implementation of a neural integrator backstepping control via recurrent Wavelet First Order Neural Network. Neural Process Lett2019; 49(3): 1629–1648.
34.
HuangKXianYZhenS, et al. Robust control design for a planar humanoid robot arm with high strength composite gear and experimental validation. Mech Syst Signal Process2021; 155: 107442.
35.
MohamedZTokhiMO. Command shaping techniques for vibration control of a flexible robot manipulator. Mechatronics2004; 14(1): 69–90.
36.
ParkJChangP-HParkH-S, et al. Design of learning input shaping technique for residual vibration suppression in an industrial robot. IEEE/ASME Trans Mechatron2006; 11(1): 55–65.
37.
ChatlatanagulchaiWKijdechDBenjalersyarnonT, et al. Quantitative feedback input shaping for flexible-joint robot manipulator. ASME J Dyn Syst Meas Control2016; 138(6): 061006.
38.
BakloutiSCourteilleELemoineP, et al. Input shaping for feed-forward control of cable-driven parallel robots. ASME J Dyn Syst Meas Control2021; 143(2): 021007.
39.
ThomsenDKSøe-KnudsenRBallingO, et al. Vibration control of industrial robot arms by multi-mode time-varying input shaping. Mech Mach Theory2021; 155: 104072.
40.
WuGZhangXZhuL, et al. Fuzzy sliding mode variable structure control of a high-speed parallel PnP robot. Mech Mach Theory2021; 162: 104349.
41.
LewisFLDawsonDMAbdallahCT. Control of robot manipulators. Prentice Hall PTR, Upper Saddle River, NJ, 1993.
42.
VivasAPoignetP. Predictive functional control of a parallel robot. Control Eng Pract2005; 13(7): 863–874.
43.
ZubizarretaAMarcosMCabanesI, et al. A procedure to evaluate extended computed torque control configurations in the Stewart–Gough platform. Robot Auton Syst2011; 59(10): 770–781.
44.
LiuKXuJGeZ, et al. Robust control of 3-DOF parallel robot driven by PMAs based on nominal stiffness model. Adv Robot2017; 31(10): 531–543.
45.
ItkisU. Control System of variable structure. Jerusalem, Israel: Keter Publishing House Jerusalem Ltd, 1976.
46.
BegonPPierrotFDauchezP. Fuzzy sliding mode control of a fast parallel robot. In Proc., 1995 IEEE International Conference on Robotics and Automation, vol. 1, 1178–1183. IEEE, 1995.
47.
QiZMcInroyJEJafariF. Trajectory tracking with parallel robots using low chattering, fuzzy sliding mode controller. J Intell Robot Syst2007; 48(3): 333–356.
48.
FeiJDingH. Adaptive sliding mode control of dynamic system using RBF neural network. Nonlinear Dyn2012; 70(2): 1563–1573.
49.
YueMWangSZhangY. Adaptive fuzzy logic-based sliding mode control for a nonholonomic mobile robot in the presence of dynamic uncertainties. Proc IMechE, Part C: J Mechanical Engineering Science2015; 229(11): 1979–1988.
50.
JiaHShangWXieF, et al. Second-order sliding-mode-based synchronization control of cable-driven parallel robots. IEEE/ASME Trans Mechatron2020; 25(1): 383–394.
51.
WuGCuiCShenH. Trajectory tracking by fuzzy-based super-twist sliding mode control of a parallel pnp robot. In: AltuzarraOKecskeḿethyA (eds) Advances in Robot Kinematics 2022. Cham: Springer International Publishing, 2022, pp.486–493.
52.
FeiJWangZPanQ. Self-constructing fuzzy neural fractional-order sliding mode control of active power filter. IEEE Trans Neural Netw Learn Syst2023; 34(12): 10600–10611.
53.
SaiedHChemoriABouriM, et al. Feedforward super-twisting sliding mode control for robotic manipulators: application to PKMS. IEEE Trans Robot2023; 39(4): 3167–3184.
54.
FeiJLiuL. Fuzzy neural super-twisting sliding-mode control of active power filter using nonlinear extended State Observer. IEEE Trans Syst Man Cybern Syst2024; 54(1): 457–470.
KayacanEKayacanERamonH, et al. Towards agrobots: trajectory control of an autonomous tractor using type-2 fuzzy logic controllers. IEEE/ASME Trans Mechatron2015; 20(1): 287–298.
57.
NguyenNPOhHKimY, et al. Fuzzy-based super-twisting sliding mode stabilization control for under-actuated rotary inverted pendulum systems. IEEE Access2020; 8: 185079–185092.
58.
ZhuDZhuPHeY. Trajectory tracking of robot manipulator with adaptive fuzzy second-order super-twisting sliding mode control. Adv Robot2023; 37(22): 1438–1445.
59.
LiZZhaiJ. Fuzzy adaptive super-twisting sliding mode asymptotic tracking control of robotic manipulators. Int J Fuzzy Syst2024; 26(1): 34–43.
60.
KeshtkarSPoznyakASHernandezE, et al. Adaptive sliding-mode controller based on the “super-twist” state observer for control of the Stewart platform. Autom Remote Control2017; 78(7): 1218–1233.
61.
ZhangFHuangP. Fuzzy-based adaptive super-twisting sliding-mode control for a maneuverable tethered space net robot. IEEE Trans Fuzzy Syst2021; 29(7): 1739–1749.
62.
SaiedHChemoriAEl RafeiM, et al. A novel model-based robust super-twisting sliding mode control of PKMS: Design and real-time experiments. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 8029–8035. IEEE, 2021.
63.
LinZCuiCWuG. Dynamic modeling and torque feedforward based optimal fuzzy PD control of a high-speed parallel manipulator. J Robot Control2021; 2(6): 527–538.
Den HartogJP. Forced vibrations with combined coulomb and viscous friction. Trans Am Soc Mech Eng1931; 53(2): 107–115.
66.
HundalMS. Response of a base excited system with Coulomb and viscous friction. J Sound Vib1979; 64(3): 371–378.
67.
ShermanJMorrisonWJ. Adjustment of an inverse matrix corresponding to a change in one element of a given matrix. Ann Math Stat1950; 21(1): 124–127.
68.
PressWHTeukolskySAVetterlingWT, et al. Numerical recipes in C: the Art of scientific computing. Cambridge, UK: Cambridge University Press, 2002.
69.
HaQPNguyenQHRyeDC, et al. Fuzzy sliding-mode controllers with applications. IEEE Trans Ind Electron2001; 48(1): 38–46.
70.
QiuZCHanJDLiuJG.Experiments on fuzzy sliding mode variable structure control for vibration suppression of a rotating flexible beam. J Vib Control2015; 21(2): 343–358.
71.
NorooziNRoopaeiMJahromiMZ. Adaptive fuzzy sliding mode control scheme for uncertain systems. Commun Nonlinear Sci Numer Simul2009; 14(11): 3978–3992.
