An Integral-Enhanced Adaptive Gradient Neural Network for k WTA and Multirobot Coordination
Abstract
Existing computational methods for the $k$ -winners-take-all ( $k$ WTA) operations often suffer from limitations in eliminating lagging errors, high computational complexity, and weak robustness. To deal with these challenges, we propose an integral-enhanced adaptive gradient neural network (IAGNN) for $k$ WTA. We demonstrate that the IAGNN integrates an adaptive coefficient to eliminate lagging errors while retaining an $O(n^{2})$ computational complexity. We proved the Lyapunov stability and robustness of the IAGNN. We provide a numerical simulation, and the results demonstrate the stability and robustness of the IAGNN. Furthermore, we implement the IAGNN in a multirobot tracking system for competitive allocation coordination, and the results demonstrate the operational feasibility and noise resistance of the IAGNN.
Fields of Study & Subjects
Adaptive Dynamic Programming Control
Neural Networks and Applications
Distributed Control Multi-Agent Systems
Publication details
- Journal / source
- IEEE Transactions on Cybernetics
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 56
- Issue
- 6
- Pages
- 3165–3176
- Publication date
- 12 January 2026
- Language
- EN