In this paper, we study the problem of consensus-based distributed Nash equilibrium (NE) seeking in a network of players represented as a directed graph, where each player aims to minimize their own local cost functions non-cooperatively. To address bandwidth constraints and limited energy, we propose a stochastic event-triggered algorithm that triggers individual players with a probability depending on certain events, thus enhancing communication efficiency through reduced continuous communication. We prove that our developed event-triggered algorithm achieves exponential convergence to the exact NE when the underlying communication graph is strongly connected. Furthermore, we establish that our proposed event-triggered communication scheme does not exhibit Zeno behavior. Finally, through numerical simulations of a spectrum access game and comparisons with existing event-triggered methods, we demonstrate the effectiveness of our proposed algorithm.

This paper investigates the problem of distributed convex optimisation under constrained communication. A novel stochastic event-triggering algorithm is shown to solve the problem asymptotically to any arbitrarily small error without exhibiting Zeno behaviour. A systematic design of the stochastic event processes is then derived from the analysis on optimality and communication rate with the help of a meta-optimisation problem. Lastly, a numerical example on distributed classification is provided to visualise the performance of the proposed algorithm in terms of convergence in optimisation error and average communication rate with comparison to other algorithms in the literature. We show that the proposed algorithm is highly effective in reducing communication rates compared with algorithms proposed in the literature.

This paper studies event-triggered consensus control for heterogenous nonlinear multi-agent systems. We present a new distributed nonlinear event-triggered control algorithm integrating basic radial basis function neural network with event-based control. We show that it can handle any unknown dynamics linear in the control input, achieving practical consensus without Zeno behaviour. A numerical example is provided to highlight the effectiveness of the proposed algorithm in terms of learning the unknown nonlinear dynamics.