Resource-aware coordination of networked autonomous vehicles (AVs) requires fast convergence, safety preservation, uncertainty compensation, and efficient high-level command refreshes under limited onboard resources. This paper proposes a graph-based self-triggered adaptive fixed-time coordination scheme for longitudinal AV formations subject to prescribed spacing and velocity constraints. A smooth nonlinear transformation maps constrained physical variables into an unconstrained domain. To accommodate heterogeneous vehicle dynamics, the transformed uncertainty of each follower is represented over a common radial basis function (RBF) dictionary by a vehicle-dependent ideal coefficient vector. Neighboring followers exchange their current coefficient estimates over a communication graph; the resulting adaptive law produces both a Laplacian disagreement-dissipation term and a graph-weighted heterogeneity term in the Lyapunov analysis. A dynamic-surface controller with homogeneous fixed-time feedback is then combined with a self-triggered mechanism that refreshes and holds the transformed-domain command. The neural estimator and neural weight exchange remain continuous, so the resource reduction claimed here concerns high-level transformed-command refreshes rather than neural communication. Lyapunov analysis establishes semi-global uniform ultimate boundedness, forward invariance of the original spacing and velocity constraints, practical fixed-time convergence with a heterogeneity-dependent residual bound, and exclusion of Zeno behavior.
This paper studies the stochastic generalized Nash equilibrium (SGNE) seeking problem for merely monotone games, where each player minimizes their own expected-value cost function subject to local and shared coupling constraints. Using the operator splitting technique, we restate the problem as seeking the zero of the sum of monotone operators. Then, we propose a novel distributed SGNE seeking algorithm based on a newly improved forward-backward-forward operator splitting, which performs one pseudo-gradient approximation computation and one communication round over the communication graph at each iteration. By theoretical analysis, we prove that the iterates generated by the designed algorithm converge to a variational SGNE almost surely if the expected value of the pseudo-gradient is approximated by the average of several random samples. Finally, the designed algorithm is also verified via numerical experiments on a wireless Ad-Hoc network.
This paper investigates the distributed adaptive prescribed-time (PT) pinning synchronization of complex networks (CNs) under event-triggered coupling communication strategies. In contrast to existing PT synchronization schemes that rely on global information, the proposed synchronization protocols for CNs are fully distributed, i.e., the pinning synchronization of CNs is achieved within a prescribed time without using any global information. To this end, both edges-based and nodes-based distributed adaptive update laws are designed to adjust the time-varying coupling strengths. Then, the all-edges-based, partial-edges-based, and node-based event-triggered PT strategies are proposed to realize synchronization while saving coupling communication resources. Furthermore, Zeno behavior is excluded. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed results.
This paper studies leaderless collective motion of a prescribed relative formation over periodically weighted directed rings. A period-2 weighting pattern (alternating positive edge weights) is shown to select the group's constant translational velocity without leaders, global references, or externally prescribed motion commands. For second-order agents, the classical modal stability condition is applied to the explicit period-2 spectrum to obtain a finite-spectrum gain threshold, whereas the collective velocity determined by the left null vector of the Laplacian remains invariant. This property is combined with relative shape regulation, yielding a distributed translation mechanism for the prescribed formation. An explicit velocity formula further reveals how the period-2 weight ratio blends the odd- and even-indexed average initial velocities, leading to a one-parameter inverse design characterization of the open segment of achievable constant velocities between the two class averages. Beyond fixed-size networks, the paper studies pair insertion and pair deletion rules that preserve the alternating topology. In particular, assigning a local velocity offset during pair insertion produces a computable direction-consistent change of the collective velocity. The post update dynamics then recover to the corresponding translating formation manifold. Comparative simulations with uniform-ring regulation, an additional thirty-agent case, and a gain-threshold sweep support the network-selected-motion interpretation and the finite-spectrum threshold.
Noticing that time delays frequently appear in many practical systems, this paper investigates game-based distributed robust control for nonlinear players whose dynamics are influenced by unknown time delays. Both first- and second-order nonlinear players' dynamics are considered in the paper. Under the partial decision information setting, the delay compensation, consensus estimation, and optimization of players' objectives are tightly coupled and need to be achieved simultaneously, which leads to two primary challenges: 1) how to design distributed strategies for players to accommodate unknown time delayed dynamics such that the Nash equilibrium (NE) can be found; 2) how to select Lyapunov-Krasovskii functions to prove the convergence. Based on adaptive control laws, gradient optimization algorithms and consensus protocols, a distributed NE seeking strategy is developed, which is able to effectively compensate the unknown time delays and nonlinear dynamics. The Lyapunov–Krasovskii stability analysis is further conducted, which confirms the effectiveness of the proposed strategies in regulating players' states to small neighborhoods around the NE, ensuring robust performance despite the presence of unknown delays. Finally, simulation studies on a network of unmanned vehicles validate the feasibility and efficacy of the proposed strategies
