This brief is concerned with the time-varying formation problem for second-order networked multi-agent systems, which are subject to random communication delays and packet dropouts in the sensor-to-controller and controller-to-actuator channels of each agent. A cloud-based time-varying formation predictive control method with control input quantization is proposed to actively compensate for those random communication constraints. A definition of quantized time-varying formation is given, and then a necessary and sufficient condition to achieve the quantized time-varying formation as well as the stability of the resulting closed-loop system is obtained, which is independent of random communication constraints and quantization errors. Simulation results are provided to verify the effectiveness of the proposed method.

In this article, the time-varying formation control problem with collision avoidance is addressed for uncertain nonlinear second-order multiagent systems in a null-space-based behavioral control architecture. To guarantee the tracking and coordination performance simultaneously, a novel and flexible time-varying formation task strategy is designed where only neighborhood information is necessary. Moreover, the agent radius and a sine function are introduced such that the collision avoidance task function describes collision risk more accurately in contrast to existing results. Then, two fixed-time sliding mode controllers with constant and variable exponent coefficients, respectively, are proposed to track the desired trajectory generated by null space projection. Also, the theoretical results for the task design and trajectory tracking are obtained by using the Lyapunov stability theory. Numerical simulation and practical experiments are finally conducted to illustrate the effectiveness and superiority of the proposed method.

This paper presents a time-varying formation tracking control scheme for second-order networked multi-agent systems with random network-induced delays and packet losses. Three types of communication channels are considered: the feedback and forward channels of each agent as well as the channels between agents. To mitigate the adverse effects of the three-channel communication constraints, a novel distributed predictive control scheme is proposed by combining the networked predictive control approach and the time-delay system approach. Then, the stability condition for the resulting closed-loop time-delay system is derived via the Lyapunov theory, which is independent of the random communication constraints in the forward and feedback channels. Finally, comparative simulation results verify the superiority of the proposed scheme.

This paper proposes a composite control method that integrates an active fault-tolerant predictive control scheme and an event-triggered mechanism for networked multi-agent systems. The approach considers random communication constraints in the forward and feedback channels as well as actuator faults. At each time instant, the event trigger determines whether to send system outputs based on the current system state. A Kalman filter is then utilized to estimate both the system state and potential faults by incorporating system output information transmitted through the feedback channel. Concurrently, iterative predictions are performed according to the established system model. Furthermore, a predictive sequence of control inputs is generated through the designed control protocol. Leveraging timestamping technology, the system precisely applies the appropriate control commands to the actuator at designated moments. As a result, the proposed control method compensates for both random communication constraints and actuator faults while effectively reducing data transmission over the communication network. Finally, the proposed method is validated through numerical simulations.

This paper investigates the tracking control problem for a class of networked nonlinear systems in a non-ideal communication environment, where both internal communication constraints (delays and packet dropouts) and external random deception attacks are taken into account. From a symmetry perspective, the backward and forward channels constitute a paired sensing–actuation structure, and channel-dependent imperfections may destroy their functional coordination. To compensate for the resulting sensing–actuation mismatch, a data-driven sliding-mode predictive tracking control scheme is developed without relying on an explicit system model. First, an equivalent dynamic linearization is adopted to represent the input–output behavior using a data-dependent incremental model. Then, using delayed measurements together with historical input–output data, an online estimator is constructed to update the pseudo partial derivative (PPD). Based on the estimated PPD, a multi-step predictor is further designed to generate the predicted outputs, and a data-driven sliding-mode predictive tracking controller is proposed by imposing a discrete reaching law on the predicted outputs. Rigorous analysis is provided to ensure the stability of the closed-loop system and to guarantee that the tracking error remains bounded, together with an explicit bound that reveals the influence of the delay horizon, estimation mismatch, and attack amplitudes. Finally, numerical simulations under square-wave and sinusoidal references validate the effectiveness and robustness of the proposed approach.

Single-modal object Re-identification (Re-ID) frequently suffers from performance degradation under complex and dynamic scene conditions. Although multi-modal object Re-ID leverages complementary information across diverse modalities to alleviate this issue, existing approaches remain highly susceptible to irrelevant background interference. To address these challenges, we propose the Spatial–Frequency Token Interaction Network (SFTINet) for object Re-ID. Specifically, SFTINet utilizes a Vision Transformer (ViT) to extract multi-modal tokens, which are subsequently processed by a Spatial–Frequency Token Interaction Module. This module captures fine-grained local details in the spatial domain while concurrently encoding global structural information in the frequency domain, thereby augmenting the multi-modal fine-grained representation capability. A dynamic token interaction mechanism further regulates cross-modal information flow via attention, facilitating the deep fusion of RGB, Near-Infrared (NIR), and Thermal Infrared (TIR) features. Additionally, a Reconstruction Loss Module minimizes the distribution discrepancy across modalities through token-level constraints, and a Token Mask Generation Module encourages foreground-centric learning by strategically masking key tokens. By seamlessly integrating spatial–frequency interaction with token-level masking, SFTINet effectively suppresses background interference while preserving cross-modal complementary information. Experimental results demonstrate that SFTINet achieves competitive performance on multi-modal benchmarks, validating its effectiveness in robust cross-modal feature fusion and fine-grained semantic modeling.