Adaptive Neural Cooperative Control of Multirobot Systems With Input Quantization
Abstract
This article develops the adaptive neural cooperative control scheme for a group of mobile robots with a limited sensing range in presence of input quantization by a dynamic surface control technique. First, to make the controller design feasible, the original robotic system is transformed into a new fully actuated system using a transverse function. Then, taking into consideration the effects of a hysteresis quantizer, an adaptive neural cooperative controller is developed based on the universal approximation property of the radial basis function neural networks and the connectivity preservation strategy. Furthermore, the proposed control scheme can guarantee that all closed-loop signals are semi-globally uniformly ultimately bounded. Meanwhile, desired constraints are not breached and tracking errors are within the predefined domains. Finally, several simulation results are carried out to testify the feasibility and efficiency of the theoretical findings revealed in this article.
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Hybrid FTC strategies integrate passive fault-tolerant control's durability with active fault-tolerant control's responsiveness, addressing complex fault profiles across cooperative robotics.
“Hybrid FTC strategies integrate PFTC's durability with AFTC's responsiveness, addressing complex fault profiles across cooperative robotics [3,29,30].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [29]
Neural-adaptive methods better handle unpredictable conditions than traditional static controllers like PID, though their reliance on GPU-intensive training limits edge deployment.
“Compared to traditional static controllers like Proportional-Integral-Derivative (PID), which struggle with nonlinearity, neural-adaptive methods better handle unpredictable conditions, though their reliance on GPU-intensive training limits edge deployment [29].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [29]
Hardware acceleration including GPUs with a 2 GHz clock and FPGA with a 100 MHz clock enables rapid responses in swarms.
“Hardware acceleration, including GPUs with a 2 GHz clock and FPGA with a 100 MHz clock, enables rapid responses in swarms [27,29]”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [29]
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- Authors
- Ma, T., Hu, F., Su, X., Shen, C., & Ma, X.
- Affiliations
- Chongqing University (CN)
- Xi'an Jiaotong University (CN)
- Sichuan University of Arts and Science (CN)
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2024-09
- Volume
- 54
- Issue
- 9
- Pages
- 5518-5528
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W4393253003
- Cited by
- 17
- References
- 36
- Citations by year
- 2026: 9 · 2025: 8
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Cybernetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (U22A20101, 62173051, CSTB2023NSCQ-MSX0452)
- National Foreign Expert Introduction Program (G2023165016L)
- International ST Cooperation Projects of Huangpu District (2022GH16)
- Chongqing Technology Innovation and Application Development Special Key Project (CSTB2022TIAD-CUX0015, CSTB2022TIAD-KPX0162)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Control and Dynamics of Mobile Robots
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Keywords
- multi-robot systems, mobile robots, input quantization, adaptive neural network control, cooperative control, dynamic surface control, hysteresis quantizer, radial basis function neural network, semi-global uniform ultimate boundedness, tracking control, connectivity preservation
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