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This article investigates distributed Nash equilibrium (NE) seeking for multirobot systems with nonlinear dynamics, time-varying disturbances, and individual inequality constraints under switching communication topologies. A novel control architecture is developed by integrating adaptive radial basis function (RBF) neural networks with projection-based pseudogradient dynamics. The proposed method enables each robot to estimate and track its local NE strategy in a fully distributed manner, without requiring global information or prior knowledge of the disturbances. Unlike existing methods that rely on static graphs or known disturbance bounds, our approach ensures constraint satisfaction and disturbance rejection simultaneously under a jointly strongly connected switching network. Numerical simulations involving five 2-degrees of freedom robotic manipulators demonstrate the effectiveness of the proposed strategy, achieving convergence to the NE within 20 s, strict adherence to inequality constraints.
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Adaptive Switching for Multimodal Underwater Acoustic Communications Based on Reinforcement Learning
The underwater acoustic (UWA) channel is a complex and stochastic process with large spatial and temporal dynamics. This work studies the adaptation of the communication strategy to the channel dynamics. Specifically, a set of communication strategies are considered, including frequency shift keying (FSK), single-carrier communication, and multicarrier communication. Based on the channel condition, a reinforcement learning (RL) algorithm, the Depth Determined Strategy Gradient (DDPG) method along with a Gumbel-softmax scheme is employed for intelligent and adaptive switching among those communication strategies. The adaptive switching is performed on a transmission block-by-block basis, with the goal of maximizing a long-term system performance. The reward function is defined based on the energy efficiency and the spectral efficiency of the communication strategies. Simulation results reveal that the proposed method outperforms a random selection method in time-varying channels.
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- Award ID(s):
- 1651135
- PAR ID:
- 10314505
- Date Published:
- Journal Name:
- the 15th International Conference on Underwater Networks & Systems (WUWNet)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1145/3491315.3491354
