This brief addresses the stability analysis problem for stochastic neural networks (SNNs) with discrete interval and distributed time-varying delays. The interval time-varying delay is assumed to satisfy 0 < d₁ ¿ d(t) ¿ d₂ and is described as d(t) = d ₁+h(t) with 0 ¿ h(t) ¿ d ₂ - d ₁. Based on the idea of partitioning the lower bound d ₁, new delay-dependent stability criteria are presented by constructing a novel Lyapunov-Krasovskii functional, which can guarantee the new stability conditions to be less conservative than those in the literature. The obtained results are formulated in the form of linear matrix inequalities (LMIs). Numerical examples are provided to illustrate the effectiveness and less conservatism of the developed results.

This paper investigates the sliding-mode control (SMC) problem of Takagi-Sugeno (T-S) fuzzy multiagent systems (MASs). A cooperative fuzzy-based dynamical sliding-mode (SM) controller is designed and the overall closed-loop T-S fuzzy MAS is constructed. A new model transformation method for T-S fuzzy MASs is presented to transform the fuzzy weighting matrix into a set of fuzzy weighting scalars. By applying the method of linear matrix inequality, a general stability analysis approach for T-S fuzzy MASs is proposed. Moreover, the energy-cost constraint problem is studied by using the linear quadratic regulator method. Finally, numerical examples are provided to illustrate the effectiveness of the proposed theoretical approaches and the improved performance compared to existing results.

Path planning is an essential technology for lunar rover to achieve safe and efficient autonomous exploration mission, this paper proposes a learning-based end-to-end path planning algorithm for lunar rovers with safety constraints. Firstly, a training environment integrating real lunar surface terrain data was built using the Gazebo simulation environment and a lunar rover simulator was created in it to simulate the real lunar surface environment and the lunar rover system. Then an end-to-end path planning algorithm based on deep reinforcement learning method is designed, including state space, action space, network structure, reward function considering slip behavior, and training method based on proximal policy optimization. In addition, to improve the generalization ability to different lunar surface topography and different scale environments, a variety of training scenarios were set up to train the network model using the idea of curriculum learning. The simulation results show that the proposed planning algorithm can successfully achieve the end-to-end path planning of the lunar rover, and the path generated by the proposed algorithm has a higher safety guarantee compared with the classical path planning algorithm.

This paper investigates the problem of distributed attitude control for spacecraft formation flying (SFF) systems based-on the Takagi-Sugeno (T-S) fuzzy modeling method, where external disturbances and parameter uncertainties are considered. The spacecraft attitude dynamics is constructed as T-S fuzzy model and a modified distributed fuzzy-based H1 controller is designed. It is proven that the attitude of multi-spacecraft can synchronously reach zero by using the designed controller. Numerical examples are provided to illustrate the effectiveness of the proposed controller and comparisons with the existing results.

Reconfigurable intelligent surface (RIS), as an emerging technology, has recently been applied to expand the range of mobile-edge computing (MEC) networks and improve wireless environments. However, current terrestrial RIS-assisted MEC networks have some limitations, such as severe signal attenuation and inflexible equipment deployment. To take full advantage of the superiority of the RIS, this article considers an aerial RIS (ARIS)-assisted aerial computing scheme, where the ARIS and the other unmanned aerial vehicle (UAV) equipped with a MEC server are employed to facilitate offloading computing tasks from Internet of Things (IoT) user equipments (UEs) to the access point (AP). With the flexibility of the dual-UAV, we can mitigate the Non-Line-of-Sight (NLoS) air–ground paths caused by obstacles. In the proposed scenario, to improve the system energy efficiency while ensuring the UEs receive high-quality wireless services, we intend to jointly optimize the trajectories of the two UAVs, the phase shift of the ARIS, the computation offloading strategy, and computation resource allocation. The issue is formulated as a mixed nonconvex optimization problem, so it is difficult to solve it in time for adapting different environments by using conventional convex optimization methods. However, we develop a double deep $Q$ -network (DDQN)-based algorithm to obtain the near-optimal online decision-making solution. Simulation findings indicate that the proposed DDQN-based algorithm can effectively increase the energy efficiency of the proposed dual-UAV cooperative MEC system in comparison to the benchmark schemes.