Adaptive backstepping control of high-order fully actuated nonlinear systems with event-triggered strategy
Abstract
This paper investigates the problem of adaptive event-triggered fuzzy control for nonlinear high-order fully actuated systems. In this paper, a completely unknown nonlinear function is considered, and its prior knowledge is unknown. To solve this problem, the fuzzy logic system technology is applied to approximate the unknown nonlinear function. In order to save communication resources, a novel high-order event-triggered controller is proposed under backstepping control. With the help of Lyapunov stability theory, it is proved that all signals of the closed-loop system are bounded. Finally, the theoretical results are applied to the robot system to verify their validity.
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Control algorithms using fuzzy logic systems or neural networks are usually computationally expensive because FLS relies on expert knowledge rules and NN requires online learning or offline training.
“However, the control algorithms using FLS/NN are usually computationally expensive, since FLS relies heavily on the knowledge rules of expert and NN requires either online learning or offline training procedures to make the controller perform properly.”
From Compensation Function Observer-Based Backstepping Sliding-Mode Control of Uncertain Electro-Hydraulic Servo System (2024) · cites this paper as [21]
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43 of the 43 references couldn’t be matched to a known paper.
Cited by
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Fully Actuated System Approach for Control: An Overview
Duan, G. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3457584 · Cited by 201 · see citing works
“The event-triggered adaptive control is considered in [64], [65], [66], [67], and [68].”
Zhang, C., Wang, L. M., Zhang, Y., et al. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3534832 · Cited by 16 · see citing works
Pan, C., Huang, H., Chen, C., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-00988-2 · Cited by 11 · see citing works
“To achieve a satisfactory tracking performance of the uncertain FJMs, researchers have developed numerous control methods, such as nonlinear PID control 10,11 , sliding mode control 12,13 , adaptive control 14,15 , robust control 16,17 , backstepping control 18–20 , and so on.”
Pan, C., Wang, Y., Yang, S. X., Li, Z., & Xiao, J. (2024) · Machines · doi:10.3390/machines12100719 · Cited by 16 · see citing works
Robust Bounded-H∞ Control for High-Order Fully Actuated Systems Based on FAS Approaches
Hu, L., & Duan, G. (2024) · Conference on Fully Actuated System Theory and Applications (FASTA) · doi:10.1109/fasta61401.2024.10595288
Periodic Event‐Triggered Predictive Tracking Control for Mobile Robots With Unknown Disturbances
Liu, A., Lin, X., Qin, D., et al. (2025) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.70032
Wang, B., Zhao, Z., Qiu, R., et al. (2026) · Journal of Vibration and Control · doi:10.1177/10775463261476306
Seilkhanova, M., & Alimhan, K. (2026) · Bulletin of Shakarim University Technical Sciences · doi:10.53360/2788-7995-2026-2(22)-8
- Authors
- Yan, C., Xia, J., Liu, X., Yue, H., & Li, C.
- Venue
- Intelligence & Robotics
- Publisher
- OAE Publishing Inc.
- Published
- 2023-06-27
- Volume
- 3
- Issue
- 2
- Pages
- 176-89
- ISSN
- 2770-3541 (ISSN-L)
- OpenAlex
- W4382895137
- Cited by
- 8
- References
- 43
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3
- FWCI
- 0.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Intelligence & Robotics (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
- Keywords
- adaptive backstepping control, event-triggered control, high-order nonlinear systems, fully actuated systems, fuzzy systems, unknown nonlinearities, communication resource saving, robot control
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