In this study, a new fuzzy robust backstepping controller with estimation is proposed for the control of a robot manipulator. Backstepping control is preferred because the Lyapunov function that is used in stability analysis and the feedback control law that is used for control purpose are defined systematically during controller design. Fuzzy logic units are designed to update the gains of the backstepping controller. Then, the proposed controller is applied to a robot manipulator that is to track a trajectory in a three-dimensional space while it is subjected to external disturbances and parameter variations. The numerical results demonstrate and verify the successful performance of the proposed controller.
ÅströmKJMurrayRM (2008) Feedback Systems: An Introduction for Scientists and Engineers. Princeton, NJ: Princeton University Press.
2.
BaruaAMudunuriLSKoshelevaO (2014) Why trapezoidal and triangular membership functions work so well: towards a theoretical explanation. Journal of Uncertain Systems8(3): 164–168.
3.
BelchiorCACAraújoRAMLandeckJA (2012) Dissolved oxygen control of the activated sludge wastewater treatment process using stable adaptive fuzzy control. Computers & Chemical Engineering37: 152–162.
4.
ChangC-FHuangC-IFuL-C (2004) Nonlinear control of a wheeled mobile robot with nonholonomic constraints. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, 10–13 October 2004, pp. 5404–5410. The Hague, Netherlands: IEEE.
5.
ChangJ-WWangR-JWangW-JHuangC-H (2015) Implementation of an object-grasping robot arm using stereo vision measurement and fuzzy control. International Journal of Fuzzy Systems17(2): 193–205.
6.
ChenBLiuXTongS (2007) Adaptive fuzzy output tracking control of MIMO nonlinear uncertain systems. IEEE Transactions on Fuzzy Systems15(2): 287–300.
7.
ChenC-LWangS-NHsiehC-TChangF-Y (1999) Theoretical analysis of a fuzzy-logic controller with unequally spaced triangular membership functions. Fuzzy Sets and Systems101(1): 87–108.
8.
ErtugrulMKaynakOSabanovicA (1995) A comparison of various vss techniques on the control of automated guided vehicles. In: Proceedings of the IEEE international symposium on industrial electronics, Athens, Greece, 10–14 July, pp. 837–842.
9.
GuzelkayaMEksinIYesilE (2003) Self-tuning of PID-type fuzzy logic controller coefficients via relative rate observer. Engineering Applications of Artificial Intelligence16(3): 227–236.
10.
HaciogluY (2015) Multiple-input multiple-output proportional-integral-proportional-derivative type fuzzy logic controller design for a twin rotor system. Journal of Vibroengineering17(6): 3104–3110.
11.
HaciogluYArslanYZYagizN (2008) PI+PD type fuzzy logic controlled dual-arm robot in load transfer. Strojniski Vestnik-Journal of Mechanical Engineering54(5): 347–355.
12.
HoHFWongYKRadAB (2008) Adaptive fuzzy approach for a class of uncertain nonlinear systems in strict-feedback form. ISA Transactions47(3): 286–299.
13.
KrsticMKanellakopoulosIKokotovicP (1995) Nonlinear and Adaptive Control Design. New York: John Wiley & Sons, Inc.
14.
LiHWangLDuHBoulkrouneA (2017) Adaptive fuzzy backstepping tracking control for strict-feedback systems with input delay. IEEE Transactions on Fuzzy Systems25(3): 642–652.
15.
LiuHPanYLiSChenY (2017) Adaptive fuzzy backstepping control of fractional-order nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems47(8): 2209–2217.
16.
MahonyRHamelT (2005) Image-based visual servo control of aerial robotic systems using linear image features. IEEE Transactions on Robotics21(2): 227–239.
17.
MamdaniEH (1974) Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers121(12): 1585–1588.
18.
Mohd BasriMAHusainARDanapalasingamKA (2015) Enhanced backstepping controller design with application to autonomous quadrotor unmanned aerial vehicle. Journal of Intelligent & Robotic Systems79(2): 295–321.
19.
NingDSunSZhangFet al. (2017) Disturbance observer based Takagi-Sugeno fuzzy control for an active seat suspension. Mechanical Systems and Signal Processing93: 515–530.
20.
RudraSBaraiRKMaitrM (2016) Design and implementation of a block-backstepping based tracking control for nonholonomic wheeled mobile robot. International Journal of Robust and Nonlinear Control26(14): 3018–3035.
21.
TaskinYHaciogluYYagizN (2007) The use of fuzzy-logic control to improve the ride comfort of vehicles. Strojniski Vestnik-Journal of Mechanical Engineering53(4): 233–240.
22.
UtkinVGuldnerJShiJ (1999) Sliding mode control in electromechanical systems. Taylor & Francis.
23.
WangR-GLiuZ-DQiZ-Qet al. (2005) Multiple model adaptive control of antilock brake system via backstepping approach. In: Proceedings of the fourth International Conference on Machine Learning and Cybernetics, 18–21 August 2005, Guangzhou, China, pp. 591–595. IEEE.
24.
YacefFBouhaliOHamerlainMRizougN (2016) Observer-based adaptive fuzzy backstepping tracking control of quadrotor unmanned aerial vehicle powered by Li-ion battery. Journal of Intelligent & Robotic Systems84(1–4): 179–197.
25.
YordanovaSJainLC (2017) Design of supervisor-based adaptive process fuzzy logic control. International Journal of Advanced Intelligence Paradigms9(4): 385–401.
26.
YuHQiZDuanJet al. (2015) Multiple model adaptive backstepping control for antilock braking system based on LuGre dynamic tyre model. International Journal of Vehicle Design69(1–4): 168–184.
27.
ZadehLA (1965) Fuzzy Sets. Information and Control8(3): 338–353.
28.
ZhaoJBoseBK (2002) Evaluation of membership functions for fuzzy logic controlled induction motor drive. In: IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02, 5–8 November 2002, Sevilla, Spain, pp. 229–234. IEEE.
