In this paper, an output feedback implicit Lyapunov control (OFILC) method is proposed to address the positioning challenges of rigid chain lifters under load variations and unmeasurable velocity conditions. OFILC represents a novel integration of a linear extended state observer (LESO) and implicit Lyapunov control (IL), a combination that has not been previously explored. Firstly, OFILC introduces friction and gravity moment compensation to reduce the uncertainty of the dynamics model. Secondly, a LESO-based velocity observer is developed to overcome the measurement limitation, providing clean state estimation without the noise amplification issues inherent in signal differentiation. Finally, to address the disturbance problem caused by parameter uncertainty and load differences, LESO is introduced to estimate and compensate for model disturbances online, thereby enhancing robustness and control accuracy compared to the IL approach. Theoretical analyses demonstrate that this controller is uniformly ultimately bounded convergence. Experimental results demonstrate that OFILC offers advantages in terms of high precision, robustness, and low power consumption with different load conditions.
DengWYaoJWangY, et al. Output feedback backstepping control of hydraulic actuators with valve dynamics compensation. Mech Syst Signal Process2021; 158: 107769.
2.
YaoJDengWJiaoZ.Adaptive control of hydraulic actuators with lugre model-based friction compensation. IEEE Trans Ind Electron2015; 62(10): 6469–6477.
3.
YaoJJiaoZMaD, et al. High-accuracy tracking control of hydraulic rotary actuators with modeling uncertainties. IEEE/ASME Trans Mechatron2014; 19(2): 633–641.
4.
DavisTAShinYCYaoB.Adaptive robust control of machining force and contour error with tool deflection using global task coordinate frame. Proc IMechE, Part B: J Eng Manuf2018; 232(1, SI): 40–50.
5.
ZhaoLCaoXLiX.Adaptive sliding-mode control for inertial reference units via adaptive tracking differentiators. IEEE Trans Syst Man Cybern Syst2023; 53(5): 3208–3218.
SumitAElamvazuthiLI.Non-linear active disturbance rejection control for upper limb rehabilitation exoskeleton. Proc IMechE Part I: J Syst Control Eng2021; 235(5): 606–632.
8.
BaoDLiangXGeSS, et al. Adaptive neural trajectory tracking control for n-dof robotic manipulators with state constraints. IEEE Trans Ind Inform2023; 19(7): 8039–8048.
9.
Xingkai FengCW. Adaptive neural network tracking control of an omnidirectional mobile robot. Proc IMechE Part I: J Syst Control Eng2023; 237(3): 375–387.
10.
Anan'yevskiiIM. Continuous feedback control of perturbed mechanical systems. J Appl Math Mech2003; 67(2): 143–156.
11.
GuoYHouB.Implicit lyapunov function-based tracking control of a novel ammunition autoloader with base oscillation and payload uncertainty. Nonlinear Dyn2021; 27(3–4): 392–403.
12.
WangXHouB.Trajectory tracking control of a 2-dof manipulator using computed torque control combined with an implicit lyapunov function method. J Mech Sci Technol2018; 32(6): 2803–2816.
13.
GuoYHouBXuS, et al. Robust stabilizing control for oscillatory base manipulators by implicit lyapunov method. Nonlinear Dyn2022; 108(3): 2245–2262.
14.
WangXHouB.Continuous time-varying feedback control of a robotic manipulator with base vibration and load uncertainty. J Vib Control2021; 27: 392–403.
15.
Lin YubinHbBaoDXinJ. Integrating deep learning modeling with implicit lyapunov function-based super-twisting sliding mode control for robust performance of a chain conveyor. Proc IMechE Part I: J Syst Control Eng2025; 239(7): 1239–1251. DOI: 10.1177/09596518251322251.
16.
LiYMaSLiK, et al. Adaptive fuzzy output feedback fault-tolerant control for active suspension systems. IEEE Trans Intell Vehicles2024; 9(1): 2469–2478.
17.
ChenLCuiRYangC, et al. Adaptive neural network control of underactuated surface vessels with guaranteed transient performance: theory and experimental results. IEEE Trans Ind Electron2020; 67(5): 4024–4035.
18.
ChenMGeSS.Adaptive neural output feedback control of uncertain nonlinear systems with unknown hysteresis using disturbance observer. IEEE Trans Ind Electron2015; 62(12): 7706–7716.
19.
SunJYangJZengZ, et al. Sampled-data output feedback control for nonlinear uncertain systems using predictor-based continuous-discrete observer. IEEE Trans Neural Netw Learn Syst2023; 34(11): 9223–9233.
20.
YangXYaoJDengW.Output feedback adaptive super-twisting sliding mode control of hydraulic systems with disturbance compensation. ISA Trans2021; 109: 175–185.
21.
DengWYaoJ.Extended-state-observer-based adaptive control of electrohydraulic servomechanisms without velocity measurement. IEEE/ASME Trans Mechatron2020; 25(3): 1151–1161.
22.
ShiJSunHHouL.Observer-based Takagi–Sugeno fuzzy sampled-data dynamic positioning controller design for unmanned surface vehicle with external disturbances and actuator faults. Proc IMechE Part I: J Syst Control Eng2023; 237(2): 294–313.
23.
ThenozhiSSanchezACRodriguez-ResendizJ.A contraction theory-based tracking control design with friction identification and compensation. IEEE Trans Ind Electron2022; 69(6): 6111–6120.
24.
WangMSongCShiW.Active disturbance rejection formation control for multi-agent systems with multiple leaders. Proc IMechE Part I: J Syst Control Eng2023; 237(3): 388–400.
25.
LiuYSunHHouL.Distributed extended state observer design and output feedback anti-disturbance control for nonlinear interconnected systems: a dynamic memory event-triggered mechanism. Asian J Control2024; 26(1): 227–245.
26.
YangGYaoJUllahN.Neuroadaptive control of saturated nonlinear systems with disturbance compensation. ISA Trans2022; 122: 49–62.
27.
RenCLiXYangX, et al. Extended state observer-based sliding mode control of an omnidirectional mobile robot with friction compensation. IEEE Trans Ind Electron2019; 66(12): 9480–9489.
28.
HanXLiuGLeY, et al. Unbalanced magnetic pull disturbance compensation of magnetic bearing systems in MSCCs. IEEE Trans Ind Electron2023; 70(4): 4088–4097.
29.
CuiYYinZLuoP, et al. Linear active disturbance rejection control of ipmsm based on quasi-proportional resonance and disturbance differential compensation linear extended state observer. IEEE Trans Ind Electron2024; 71(10): 11910–11924.
30.
LuoCYaoJGuJ.Extended-state-observer-based output feedback adaptive control of hydraulic system with continuous friction compensation. J Franklin Inst2019; 356(15): 8414–8437.
31.
CanudasdeWitCOlssonHAstromKJ, et al. A new model for control of systems with friction. IEEE Trans Automat Contr1995; 40(3): 419–425.
32.
HuXHanSLiuY, et al. Two-axis optoelectronic stabilized platform based on active disturbance rejection controller with lugre friction model. Electronics2023; 12(5): 1261.
33.
ZhangXDaiKZhangB, et al. Adaptive backstepping-extended state observer control for electrohydraulic position servo systems with multiple disturbances using the compound friction compensation. Proc Inst Mech Eng Part I: J Syst Control Eng2024; 238(4): 583–595.
34.
SobczykMRGerviniVIPerondiEA, et al. A continuous version of the lugre friction model applied to the adaptive control of a pneumatic servo system. J Franklin Inst2016; 353(13): 3021–3039.
35.
LuLYaoBWangQ, et al. Adaptive robust control of linear motors with dynamic friction compensation using modified lugre model. Automatica2009; 45(12): 2890–2896.
36.
ZhengLQGaoZ. On stability analysis of active disturbance rejection control for nonlinear time-varying plants with unknown dynamics. In: 2007 46th IEEE conference on decision and control, 2007, pp.3501–3506. IEEE.
37.
MakkarCDixonWSawyerW, et al. A new continuously differentiable friction model for control systems design. In: Proceedings, 2005 IEEE/ASME international conference on advanced intelligent mechatronics, 2005, pp.600–605. IEEE.
38.
YaoJJiaoZMaD.Adaptive robust control of dc motors with extended state observer. IEEE Trans Ind Electron2014; 61(7): 3630–3637.
39.
YangXHuangY. Capabilities of extended state observer for estimating uncertainties. In: 2009 American control conference, 2009, pp.3700–3705. DOI:10.1109/ACC.2009.5160642.
40.
PolyakovAEfimovDPerruquettiW.Finite-time and fixed-time stabilization: Implicit Lyapunov function approach. Automatica2015; 51: 332–340.
41.
ZhuQ.Nonlinear systems. MDPI-Multidisciplinary Digital Publishing Institute, 2023.
