This study investigates the H∞ anti-disturbance dynamic memory event-triggered (ADDMET) control issue for Robotic Manipulator System (RMS) under multiple disturbances. An affine model is adopted to characterize the RMS, accounting for payload variations and complex operating conditions. An observer is designed to estimate and compensate for disturbances caused by modeling uncertainties and external perturbations. Based on the proposed observer, a dynamic memory event-triggered mechanism (DMETM) and an H∞ anti-disturbance event-triggered controller are developed. This conserves communication resources and reduces control effort. Sufficient conditions for the solvability of the H∞ ADDMET control problem are derived. Finally, a numerical simulation is established based on the affine model to validate the effectiveness of the proposed method.
- Open Access
- Article
Anti-Disturbance Dynamic Memory Event-Triggered Control for Robotic Manipulator Systems
- Pengfei Zhang 1,
- Ying Zhao 1,*,
- Changyi Xu 2,
- Chao Zhang 3
Author Information
Received: 13 Apr 2026 | Revised: 06 May 2026 | Accepted: 06 May 2026 | Published: 10 Aug 2026
Abstract
Keywords
robotic manipulator system | affine models | anti-disturbance | dynamic memory event-triggered control
References
- 1.
Khan, G.D. Adaptive neural network control framework for industrial robot manipulators. IEEE Access 2024, 12, 63477–63483.
- 2.
Ueda, J.; Kwon, H. Perfectly undetectable reflection and scaling false data injection attacks via affine transformation on mobile robot trajectory tracking control. IEEE Trans. Robot. 2025, 41, 6403–6418.
- 3.
Tang, J.; Li, J.; Zhou, J.; et al. Prescribed-time affine formation control for nonholonomic constrained robots. In Proceedings of the 2025 4th Conference on Fully Actuated System Theory and Applications (FASTA), Nanjing, China, 4–6 July 2025; pp. 712–717.
- 4.
Nubert, J.; Kohler, J.; Berenz, V.; et al. Safe and fast tracking on a robot manipulator: Robust MPC and neural network control. IEEE Robot. Autom. Lett. 2020, 5, 3050–3057.
- 5.
Cisneros, P.S.G.; Sridharan, A.; Werner, H. Constrained predictive control of a robotic manipulator using quasi-LPV representations. IFAC-PapersOnLine 2018, 51, 118–123.
- 6.
San-Miguel, A.; Puig, V.; Alenya, G. Disturbance observer-based LPV feedback control of a N-DoF robotic manipulator including compliance through gain shifting. Control Eng. Pract. 2021, 115, 104887.
- 7.
Zhang, C.; Wu, Y.Q. P-Rob six-degree-of-freedom robot manipulator dynamics modeling and anti-disturbance control. IEEE Access 2021, 9, 141403–141420.
- 8.
Xie, H.H.; Dai, L.; Lu, Y.C.; et al. Disturbance rejection MPC framework for input-affine nonlinear systems. IEEE Trans. Autom. Control 2022, 67, 6595–6610.
- 9.
Cheng, J.; Wu, Y.Y.; Wu, Z.G.; et al. Nonstationary filtering for fuzzy Markov switching affine systems with quantization effects and deception attacks. IEEE Trans. Syst. Man Cybern. Syst. 2022, 52, 6545–6554.
- 10.
Wang, M.; Qiu, J.B.; Yan, H.C.; et al. Data-driven control for discrete-time piecewise affine systems. Automatica 2023, 155, 111168.
- 11.
Li, Z.Y.; Zhao, J. A Zeno-free event-triggered control strategy for asymptotic stabilization of switched affine systems. IEEE Trans. Autom. Control 2022, 67, 5509–5516.
- 12.
Martinelli, A.; Gargiani, M.; Draskovic, M.; et al. Data-driven optimal control of affine systems: A linear programming perspective. IEEE Control Syst. Lett. 2022, 6, 3092–3097.
- 13.
Chang,W.M.; Li, Y.M.; Tong, S.C. Adaptive fuzzy backstepping tracking control for flexible robotic manipulator. IEEE/CAA J. Autom. Sin. 2021, 8, 1923–1930.
- 14.
Xu, X.Z.; Zhu, Y.Z.; Wu, F.; et al. Sampled-data control for buck-boost converter using a switched affine systems approach. IEEE Trans. Circuits Syst. I Regul. Pap. 2024, 71, 3380–3389.
- 15.
Liao, F.; Zhu, Y.Z.; He, X.; et al. Fault estimation of continuous-time switched affine systems with actuator faults under dwell time constraint. Int. J. Robust Nonlinear Control 2024, 34, 1417–1432.
- 16.
Hashemi, S.M.; Abbas, H.S.; Werner, H. Low-complexity linear parameter-varying modeling and control of a robotic manipulator. Control Eng. Pract. 2012, 20, 248–257.
- 17.
Pan, M.X.; Xu, Y.; Gu, B.B.; et al. Fuzzy-set theoretic control design for aircraft engine hardware-in-the-loop testing: Mismatched uncertainty and optimality. IEEE Trans. Ind. Electron. 2022, 69, 7223–7233.
- 18.
Li, P.; Zhao, X.D.; Qin, H.Q. Event-triggered model predictive control for aero-engine transient-state tracking problems. J. Aerosp. Inf. Syst. 2023, 20, 229–239.
- 19.
Xie, J.; Yang, D.; Zhao, J. Composite anti-disturbance model reference adaptive control for switched systems. Inf. Sci. 2019, 485, 71–86.
- 20.
Zhang, B.; Zhang, T.; Xi, Z.S.; et al. Estimate-based dynamic memory-event-triggered control for nonlinear networked control systems subject to hybrid attacks. Mathematics 2025, 13, 2829.
- 21.
Yao, X.M.; Guo, L. Composite anti-disturbance control for Markovian jump nonlinear systems via disturbance observer. Automatica 2013, 49, 2538–2545.
- 22.
Xu, G.Y.; Yin, L.P. Event-triggered adaptive sliding mode control for robotic arms with predefined-time convergence. In Proceedings of the 2025 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Hangzhou, China, 14–18 July 2025.
- 23.
Yan, T.; Zhao, X.; Zhang, Y.; et al. Adaptive predefined-time event-triggered consensus tracking control for robotic multi-agent system. Int. J. Intell. Control Syst. 2025, 30, 16–26.
- 24.
Chu, C.Y.; He, Y.Q. A unified neural event-triggered control approach of high-order switched uncertain systems with time-varying state constraints. Robot. Intell. Autom. 2026, 46, 290–302.
- 25.
Li, P.; Zhao, X.D.; Qin, H.; et al. Dynamic event-triggered finite-time H∞ tracking control of switched LPV aero-engine models. IEEE Trans. Circuits Syst. II Express Briefs 2022, 69, 1114–1118.
- 26.
Hou, Q.H.; Dong, J.X. Finite-time membership function-dependent H∞ control for T-S fuzzy systems via a dynamic memory event-triggered mechanism. IEEE Trans. Fuzzy Syst. 2023, 31, 4075–4084.
- 27.
Wang, T.Y.; Chen, Y.D. Event-triggered control of flexible manipulator constraint system modeled by PDE. Math. Biosci. Eng. 2023, 20, 10043–10062.
- 28.
Ma, Y.K.; Tang, W.T.; Fei, Z.Y.; et al. Dynamic event-triggered sensor fault interval estimation and accommodation for aeroengine. IEEE Trans. Ind. Electron. 2023, 71, 4191–4200.
- 29.
Gao, Y.X.; Zhao, Y.; Sang, H.; et al. Dynamic event-triggered-based anti-disturbance control for uncertain LPV systems. Int. J. Robust Nonlinear Control 2023, 33, 11275–11290.
- 30.
Duan, R.; Yu, H. LMIs in Control Systems: Analysis, Design and Applications; CRC Press: Boca Raton, FL, USA, 2013.
- 31.
Dhayal, R.; G´omez-Aguilar, J.F. Successive approximation and stability analysis of Hilfer fractional stochastic differential systems with application to circuits. Int. J. Syst. Sci. 2026, 57, 361–371.
- 32.
Vijayakumar, V.; Nisar, K.S.; Chalishajar, D.; et al. A note on approximate controllability of fractional semilinear integrodifferential control systems via resolvent operators. Fractal Fract. 2022, 6, 73.
- 33.
Elgroud, N.; Dhayal, R.; Malik, M. Approximate controllability of damped impulsive fractional stochastic differential systems driven by Levy process. Stoch. Dyn. 2026, 26, 2650001.
- 34.
Dhanush, A.; Vijayakumar, V. A class of higher-order fractional stochastic differential inclusions and feedback control results. Appl. Anal. 2026, 1–28. https://doi.org/10.1080/00036811.2026.2659840.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Suite 4002 Level 4, 447 Collins Street, Melbourne, Victoria 3000, Australia
General Inquiries: info@sciltp.com