This paper develops a precise micro-amplitude force output device (PMAFOD) and its control strategy to meet the needs of precision-instrument environmental simulation and active vibration suppression. Building on adaptive inverse control (AIC) and active disturbance-rejection control (ADRC), a disturbance-compensation adaptive inverse control (DCAIC) scheme is proposed for fast time-domain force tracking. A linear extended state observer (LESO) is used to construct the disturbance-compensation loop, which explicitly targets the system’s frequency response, mitigates AIC divergence caused by PMAFOD phase lag, and preserves ADRC’s disturbance-rejection capability. Experiments on the PMAFOD show that fixed- and sweep-frequency tracking achieves an effective bandwidth of 20–500 Hz with tracking errors approaching the 1 mN measurement noise floor, while band-limited random-waveform tracking maintains errors below 10 mN. The proposed framework provides a practical solution for broadband micro-force generation and can be extended to other precision actuation platforms.
CaiKTianYLiuX, et al. (2019) Development and control methodologies for 2-DOF micro/nano positioning stage with high out-of-plane payload capacity. Robotics and Computer-Integrated Manufacturing56: 95–105. https://doi.org/10.1016/j.rcim.2018.08.007
2.
CastañedaLALuviano-JuárezAChairezI (2015) Robust trajectory tracking of a Delta robot through adaptive active disturbance rejection control. IEEE Transactions on Control Systems Technology23: 1387–1398. https://doi.org/10.1109/tcst.2014.2367313
3.
ChenZWangYSunM, et al. (2018) Convergence and stability analysis of active disturbance rejection control for first-order nonlinear dynamic systems. Transactions of the Institute of Measurement and Control41: 2064–2076. https://doi.org/10.1177/0142331218794812
4.
ChenMSunJKarimiHR (2019) Input-output finite-time generalized dissipative filter of discrete time-varying systems with quantization and adaptive event-triggered mechanism. IEEE Transactions on Cybernetics50(12): 5061–5073. Available at: https://doi.org/10.1109/TCYB.2019.2932677.
5.
DaiXHuYCuiD, et al. (2022) A disturbance decoupling generalized proportional–integral observer design for robust sensor fault detection. IEEE Transactions on Industrial Electronics70(6): 6326–6336. https://doi.org/10.1109/tie.2022.3194645
6.
DongQLiuYZhangY, et al. (2018) Improved ADRC with ILC control of a CCD-based tracking loop for fast steering mirror system. IEEE Photonics Journal10: 1–14. https://doi.org/10.1109/jphot.2018.2846287
7.
El-SousyFFMAbuhaselKA (2016) Self-organizing recurrent fuzzy wavelet neural network-based mixed H2/H∞ adaptive tracking control for uncertain two-axis motion control system. IEEE Transactions on Industry Applications52: 5139–5155. https://doi.org/10.1109/tia.2016.2591901
8.
GaoZ (2003) Scaling and bandwidth-parameterization based controller tuning. In: Proceedings of the 2003 American Control Conference, held in Denver, CO, USA. June4–6, pp. 4989–4996.
9.
GrodsinskyCMWhortonMS (2000) Survey of active vibration isolation systems for microgravity applications. Journal of Spacecraft and Rockets37: 586–596. https://doi.org/10.2514/2.3631
10.
GuoZTianYLiuX, et al. (2015) An inverse Prandtl–Ishlinskii model based decoupling control methodology for a 3-DOF flexure-based mechanism. Sensors and Actuators A: Physical230: 52–62. https://doi.org/10.1016/j.sna.2015.04.018
11.
HanJ (2009) From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics56: 900–906.
12.
HanJHuangY (2000) Analysis and design for the second order nonlinear continuous extended states observer. Chinese ence Bulletin45: 1938–1944. https://doi.org/10.1007/bf02909682
JiangBKarimiHRYangS, et al. (2021) Observer-based adaptive sliding mode control for nonlinear stochastic markov jump systems via T–S fuzzy modeling: applications to robot arm model. IEEE Transactions on Industrial Electronics68: 466–477. https://doi.org/10.1109/tie.2020.2965501
15.
KimSHsiaoYHRenZ, et al. (2025) Acrobatics at the insect scale: a durable, precise, and agile micro–aerial robot. Science Robotics10(98): eadp4256. https://doi.org/10.1126/scirobotics.adp4256
16.
LiQLiuL (2020) Broadband modeling and precise control of piezoelectric sensing-actuating system for dynamic force output. Journal of the Franklin Institute357: 4524–4542. https://doi.org/10.1016/j.jfranklin.2020.01.030
LiuXCaoHWeiW, et al. (2020) A practical precision control method base on linear extended state observer and friction feedforward of permanent magnet linear synchronous motor. IEEE Access8: 68226–68238. https://doi.org/10.1109/access.2020.2986711
19.
LiuMZhangFHeL, et al. (2024) Dynamic neural network for motion/force control of manipulators with polynomial noises. IEEE Transactions on Industrial Electronics71(10): 12559–12569. https://doi.org/10.1109/tie.2024.3357880
20.
LuoJXuCWuJ, et al. (2025) Precise and dexterous robotic manipulation via human-in-the-loop reinforcement learning. Science Robotics10(105): eads5033. https://doi.org/10.1126/scirobotics.ads5033
21.
MorganD (1980) An analysis of multiple correlation cancellation loops with a filter in the auxiliary path. IEEE Transactions on Acoustics, Speech, & Signal Processing28: 454–467. https://doi.org/10.1109/tassp.1980.1163430
22.
RojasHDCortés-RomeroJ (2023) On the equivalence between generalized proportional integral observer and disturbance observer. ISA Transactions133: 397–411. https://doi.org/10.1016/j.isatra.2022.06.032
23.
SafaAAbdolmalakiRYNejadHC (2019) Precise position tracking control with an improved transient performance for a linear piezoelectric ceramic motor. IEEE Transactions on Industrial Electronics66: 3008–3018. https://doi.org/10.1109/tie.2018.2840523
24.
SharmaSKSharmaRCLeeJ (2025) Propelling precision of longitudinal vibration mitigation in ship propeller shafts through advanced nonlinear intelligent semi-active control leveraging adaptive neuro-fuzzy inference system with linear quadratic regulator. Journal of Vibration and Control31(7-8): 1472–1484. https://doi.org/10.1177/10775463241244836
25.
TangHLiY (2013) Design, analysis, and test of a novel 2-DOF nanopositioning system driven by dual mode. IEEE Transactions on Robotics29: 650–662. https://doi.org/10.1109/tro.2013.2248536
26.
VaudreyMABaumannWTSaundersWR (2003) Stability and operating constraints of adaptive LMS-based feedback control. Automatica39: 595–605. https://doi.org/10.1016/s0005-1098(02)00288-1
27.
WangHLiQZhouF, et al. (2024) High-precision positioning stage control based on a modified disturbance observer. Sensors24(2): 591. https://doi.org/10.3390/s24020591
28.
WidrowBWalachE (2007) Adaptive Inverse Control: A Signal Processing Approach, Reissue Edition. Wiley-IEEE Press.
29.
WuDChenK (2013) Frequency-domain analysis of nonlinear active disturbance rejection control via the describing function method. IEEE Transactions on Industrial Electronics60: 3906–3914. https://doi.org/10.1109/tie.2012.2203777
30.
XueWMadonskiRLakomyK, et al. (2017) Add-On module of active disturbance rejection for set-point tracking of motion control systems. IEEE Transactions on Industry Applications53: 4028–4040. https://doi.org/10.1109/tia.2017.2677360
ZhaoZGuoB (2018) A novel extended state observer for output tracking of MIMO systems with mismatched uncertainty. IEEE Transactions on Automatic Control63: 211–218. https://doi.org/10.1109/tac.2017.2720419
33.
ZhaoDDingSXKarimiHR, et al. (2019) On robust kalman filter for two-dimensional uncertain linear discrete time-varying systems: a least squares method. Automatica99: 203–212. https://doi.org/10.1016/j.automatica.2018.10.029
34.
ZhouRTanW (2019) Analysis and tuning of general linear active disturbance rejection controllers. IEEE Transactions on Industrial Electronics66: 5497–5507. https://doi.org/10.1109/tie.2018.2869349
35.
ZuoYZhuXQuanL, et al. (2019) Active disturbance rejection controller for speed control of electrical drives using phase-locking loop observer. IEEE Transactions on Industrial Electronics66: 1748–1759. https://doi.org/10.1109/tie.2018.2838067
