Quasi-Synchronization Control of Discrete-Time Leader–Follower Neural Networks with Parameter Uncertainties and Markovian Channel Fading
Lanzhen Chen, Hongxia Rao · Applied Sciences · 2025
Leader–follower neural networks deployed over wireless platforms are subject to parameter uncertainties and stochastic channel fading. The combined impact of these effects on quasi-synchronization control remains largely unexplored. The paper addresses the problem of quasi-synchronization performance degradation in discrete-time leader–follower neural networks caused by randomly occurring parameter uncertainties and stochastic channel fading. Discrete leader–follower neural networks are constructed in state-space form. Randomly occurring parameter uncertainties in the leader neural networks are described using a Bernoulli probability distribution and time-varying parameter matrices. Channel fading is represented by a finite-state Markovian model that captures state switching. For the follower neural networks, an intermittent impulsive control strategy is designed based on linear matrix inequalities and the Lyapunov stability principle. A computable bound on the synchronization error is derived as well. A simulation study validates that the proposed impulsive control strategy effectively suppresses synchronization error caused by parameter uncertainties and Markovian channel fading, thereby ensuring mean-square boundedness. Compared with an existing method, the proposed approach consumes less control energy but achieves better performance in terms of synchronization error. The average norms of the synchronization error and the control input signal are reduced by 24.00% and 58.64%, respectively.