In this paper, we characterize the joint effects of plant uncertainty, transmission delay and fading channel on the stabilization problem of networked control systems (NCSs). We assume that the controller remotely controls the plant and the control packet is transmitted over a delayed and fading channel. We formulate the integrated system as an uncertain discrete-time stochastic system with both input delay and multiplicative noise. The innovative contributions of this paper are described as follows. We propose a set of necessary and sufficient conditions for stabilizing an uncertain-free system. Moreover, for the general uncertain model, we derive the necessary and sufficient stabilization condition in terms of symmetric matrix homogeneous polynomials, which can be verified by LMI feasibility test. As an application, under the parallel transmission strategy, we derive an explicit formula for computing the maximum allowable delay bound, which is uniquely determined by system parameter and signal-to-noise ratio.

In this article, adaptive state feedback stabilization is addressed for a class of delayed uncertain systems with external disturbance, where the time delays and the bound parameters of the delayed states and disturbance are assumed to be unknown. By introducing a new Lyapunov–Krasovskii functional, we present a memoryless control strategy to stabilize nonlinear time-delay systems through newly defined control gain function. The main contribution of this article lies in the construction of control gain function. Multiple gain functions can be revealed in a function set. The asymptotic stability of the closed delayed nonlinear system is ensured. Simulation examples are presented to show the effectiveness of the proposed method.

In this paper, we address the problem of exponential mean-square stabilization control for cyber-physical systems (CPSs) characterized by time-varying parameters, random denial-of-service (DoS) attacks, and transmission delay. The motivation arises from the increasing complexity and vulnerability of CPSs, which are prone to various networked challenges, including random DoS attacks and transmission delays. Additionally, the presence of time-varying parameters further exacerbates the difficulty in designing stabilizing control strategies. To tackle this challenging issue, we propose two distinct approaches. First, we utilize the frozen technique, leading to the derivation of two explicit sufficient conditions. This technique exploits the concept of “freezing” the time-varying system parameters, providing an novel way to circumvent the construction of Lyapunov functions. The second method involves extending the generalized spectral radius to CPSs for the first time, allowing us to obtain an easily computable adjoint spectral radius. With this extension, we establish necessary and sufficient conditions for achieving exponential stabilization. The effectiveness of our proposed methods are illustrated through two simulation examples.

There are many constraints for a multi-robot system to perform a region coverage search task in an unknown environment. To address this, we propose a novel multi-robot distributed collaborative region coverage search algorithm based on Glasius bio-inspired neural network (GBNN). Firstly, we develop an environmental information updating model to represent the dynamic search environment. This model converts the environmental information detected by the robot into dynamic neural activity landscape of GBNN. Secondly, we introduce the distributed model predictive control method in search path planning to improve search efficiency. In addition, we propose a distributed collaborative decision-making mechanism among the robots to produce several dynamic search sub-teams. Within each sub-team, collaborative decisions are made among the robot members to optimize the solution and obtain the next movement path of each robot. Finally, we conduct experiments in three aspects to verify the effectiveness of the proposed method. Compared with three algorithms in this field, the experimental results demonstrate that the proposed algorithm exhibits good performance in a multi-robot region coverage search task.