This study investigates an improved observer-based position tracking control framework for robot manipulators (RM) operating under uncertainties and sudden variations in the control environment. An adaptive velocity observer is incorporated into the control design to estimate joint velocities without requiring direct velocity measurements. To enhance adaptability and robustness, adaptive estimation and self-updating algorithms are developed for both the observer gains and the control gains, allowing the proposed scheme to accommodate unknown dynamics and time-varying disturbances. In addition, a compensator-typed robust term is introduced to mitigate the effects of inevitable estimation and updating errors. The stability and tracking performance of the overall observer-based control system are rigorously guaranteed via Lyapunov-based analysis. Comparative simulation studies under abrupt environmental changes and external disturbances demonstrate that the proposed approach achieves effective and reliable tracking performance when compared with related observer-based and robust control methods.
NicosiaSTomeiP. Robot control by using only joint position measurements. IEEE Trans Automat Contr1990; 35(9): 1058–1061.
2.
WangXChenZQYuanZZ. Output tracking based on extended observer for nonlinear uncertain systems. Control Decis2004; 19(10): 1113–1116.
3.
Moreno-ValenzuelaJ. Velocity field control of robot manipulators by using only position measurements. J Franklin Inst2007; 344(8): 1021–1038.
4.
Acosta LúaCDi GennaroSBerenice Flores JiménezA, et al. Robust dynamic control for electric vehicles with estimation of parametric uncertainties and external disturbances. IEEE Access2024; 12: 170011–170026.
5.
RomeroJGNuñoERestrepoE, et al. Global consensus-based formation control of nonholonomic mobile robots with time-varying delays and without velocity measurements. IEEE Trans Automat Contr2024; 69(1): 355–362.
6.
XuSWuZ. Sensing and suppressing abrupt disturbances for robot manipulators via supervisory disturbance observer. IEEE Sens J2024; 24(18): 29065–29073.
7.
HanLMaoJZhangC, et al. A systematic trajectory tracking framework for robot manipulators: an observer-based non-smooth control approach. IEEE Trans Ind Electron2024; 71(9): 11104–11114.
8.
DasTKarINChaudhuryS. Simple neuron-based adaptive controller for a nonholonomic mobile robot including actuator dynamics. Neurocomputing2006; 69(16-18): 2140–2151.
9.
HwangJPKimE. Robust tracking control of an electrically driven robot: adaptive fuzzy logic approach. IEEE Trans Fuzzy Syst2006; 14(2): 232–247.
10.
WaiRJHuangYCYangZW, et al. Adaptive fuzzy-neural-network velocity sensor less control for robot manipulator position tracking. IET Control Theory Appl2010; 4(6): 1079–1093.
11.
WangXTanCPWuF, et al. Fault-tolerant attitude control for rigid spacecraft without angular velocity measurements. IEEE Trans Cybern2021; 51(3): 1216–1229.
12.
YeDXiaoYSunZ, et al. Neural network-based finite-time attitude tracking control of spacecraft with angular velocity sensor failures and actuator saturation. IEEE Trans Ind Electron2022; 69(4): 4129–4136.
13.
YangGShiZWangH, et al. Multilayer neuroadaptive output feedback control of constrained robotic manipulators with disturbance compensation. IEEE Trans Circuits Syst II Express Briefs2024; 71(2): 727–731.
14.
MaHRenHZhouQ, et al. Observer-based neural control of n-link flexible-joint robots. IEEE Trans Neural Netw Learn Syst2024; 35(4): 5295–5305.
15.
SinghRKeshavanJ. Approximation-free robust tracking control of unknown redundant manipulators with prescribed performance and input constraints. IEEE Trans Syst Man Cybern Syst2024; 54(11): 6743–6755.
16.
SuJLamHKLiuJ, et al. Fuzzy observer-based command filtered adaptive control of flexible joint robots with time-varying output constraints. IEEE Trans Circuits Syst II Express Briefs2024; 71(9): 4251–4255.
17.
ShiQWenHLiC, et al. Observer-based gate recurrent learning for model-free control of 6-DOF manipulators: considerations for input saturation and output constraints. IEEE Trans Ind Electron2024; 71(12): 16534–16545.
18.
ZhouCJOgataKHayakawaY, et al. Adaptive tracking control of rigid robot manipulators by using only joint position measurements. IFAC Proc Volumes1997; 30(11): 897–902.
19.
MarcoAAAlethiaOEJoséGR, et al. On the adaptive control of robot manipulators with velocity observers. Int J Robust Nonlinear Control2020; 30(11): 4165–4448.
20.
LeeCRParkJKKimSK, et al. Velocity observer-based nonlinear self-tuning position stabilizer for ball-beam system applications. IEEE Trans Circuits Syst II Express Briefs2020; 67(7): 1309–1313.
21.
Arteaga-PerezMAPliego-JimenezJRomeroJG. Experimental results on the robust and adaptive control of robot manipulators without velocity measurements. IEEE Trans Control Syst Technol2020; 28(6): 2770–2773.
22.
YanJGuoZYangX, et al. Finite-time tracking control of autonomous underwater vehicle without velocity measurements. IEEE Trans Syst Man Cybern Syst2022; 52(11): 6759–6773.
23.
LinWZhangZYuX, et al. Adaptive extended state observer-based velocity-free servo tracking control with friction compensation. IEEE Trans Syst Man Cybern Syst2024; 54(1): 2–11.
24.
RsetamKCaoZManZ. Design of robust terminal sliding mode control for underactuated flexible joint robot. IEEE Trans Syst Man Cybern Syst2022; 52(7): 4272–4285.
25.
ZhangDHuJChengJ, et al. A novel disturbance observer based fixed-time sliding mode control for robotic manipulators with global fast convergence. IEEE/CAA J Automatica Sinica2024; 11(3): 661–672.
26.
SuWWangQWangJ, et al. Sliding mode control of robotic manipulators with a fixed-time disturbance observer. In: 2025 International conference on information and automation (ICIA), Lanzhou, China, 2025, pp. 371-376.
27.
LewisFLDawsonDMAbdallahCT (eds). Robot manipulator control: theory and practice. 2nd ed. Prentice Hall, 2004.
MaiTTranH. An adaptive robust backstepping improved control scheme for mobile manipulators robot. ISA Trans2023; 137: 446–456.
30.
LongMTToanTHSuongNTK. Adaptive fault control for robot manipulators based on nonsingular terminal sliding mode control technique. Proc Inst Mech Eng C J Mech Eng Sci2024; 238(6): 2215–2227.
31.
AhrensJHKhalilHK. High-gain observers in the presence of measurement noise: a switched-gain approach. IFAC Proc Volumes2008; 41(2): 7606–7611.
