This article explores global consensus tracking control for multiagent systems with unknown time-varying gains and nonlinearities, subject to quantitative performance constraints. The challenge lies in designing a controller that achieves global consensus without relying on the system's initial conditions while also ensuring the prescribed settling time and the convergence accuracy of synchronization errors. First, a concise, differentiable, piecewise continuous regulation function is proposed. The combination of this regulation function with error transformation addresses the singularity issue associated with initial conditions. Then, a piecewise performance function is also introduced to quantify both settling time and steady-state accuracy of synchronization errors. Integrating the regulation and performance functions yields a novel, low-complexity, robust method for enforcing quantitative performance constraints. This approach ensures global consensus under specified constraints and eliminates the need for nonlinear function approximation, parameter estimation, higher order derivative computation, or adaptive law design. Finally, the effectiveness of the proposed method is demonstrated through comparative simulations involving a planar robotic system.

In the real world, hybrid systems with multiple modes are ubiquitous, such as the air–ground combat system, the pilot-operated aircraft and the computer-controlled drone cooperate in combat systems, traffic control systems with multiple modes, etc, which themselves have some interfering factors. So, this article explores the issue of collaborative consistency tracking of hybrid multiple agent systems (MASs) with slow time varying disturbance signals and directed communication topology. Initially, a dynamical model for the hybrid MAS with STVDSs is constructed, incorporating both first-order discrete time (FODT) and second-order continuous time (SOCT) agents. Subsequently, the constructed disturbance observers for both FODT and SOCT agents are introduced. The method under consideration is adept at detecting disturbance signals and estimating their velocities, facilitating compensation of the system. Concurrently, a class of sliding mode controllers, grounded in disturbance compensation, is proposed to accomplish collaborative consistency tracking of the hybrid MAS. It can reduce the coefficients of sliding mode functions, thereby reducing chattering. The stability of the hybrid MAS is validated using the Lyapunov method. Furthermore, the sufficient and necessary conditions is provided for the successful realization of collaborative consistency tracking. In the end, under directed communication topology without and with unidirectional ring of hybrid MASs, simulation examples provided serve to further validate the validity of the tracking process theoretical results.

Wireless sensor networks (WSNs), the fundamental sensing layer of the Internet of Things, are widely deployed in environmental monitoring, smart agriculture, and other fields. However, sensor nodes rely on limited battery power, and the intertwined issues of uneven energy consumption, node failure, and data loss in complex environments severely constrain network reliability and lifetime. To address these challenges, this article proposes an adaptive energy-efficient strategy based on location density awareness in wireless sensor networks (AEELD) protocol. This protocol introduces and optimizes the application of the clustering by fast search and find of density peaks (CFSFDPs) algorithm in WSNs, enhancing the cluster head selection process through improvements to the CFSFDP algorithm. In addition, an adaptive data transmission round adjustment mechanism is designed to dynamically regulate the number of transmission rounds based on the results of each clustering process. Furthermore, a cluster head state optimization mechanism is proposed, categorizing the energy levels of cluster heads. Data transmission is activated only when the energy meets specific requirements, effectively preventing network data loss and improving the utilization of limited node energy. Experimental results demonstrate that, compared to low-energy adaptive clustering hierarchy (LEACH), improved LEACH, K-means algorithm supported by LEACH-KMe, and balanced residual energy-LEACH protocols, AEELD exhibits superior performance in key metrics such as network lifetime, total effective data transmission volume, and energy consumption balance among nodes, validating its effectiveness in practical deployments.

This article explores the resilient fault-tolerant cluster consensus control problem for the repeatable nonlinear multiagent systems under actuator faults and denial-of-service (DoS) attacks. To circumvent unknown agent dynamics and capitalize on prior operation data, the data mapping of agents with fault information is established along the iteration domain by the locally dynamic linearization technique. In the cyber layer, stochastic DoS attacks are assumed to follow the Bernoulli distribution, and a backward attack compensation mechanism is introduced. In the physical layer, an adaptive actuator fault compensation strategy derived from the improved projection algorithm is built. Within this design, a data-driven fault-tolerant and intrusion-tolerant iterative learning control (FTIT-ILC) method is formulated to ensure dual guarantees. The convergence analysis condition of the method is presented by the nature of irreducible sub-stochastic matrices. Finally, experiments verify the FTIT-ILC method.

This article concentrates on how to achieve fast distributed optimization for multiple Euler–Lagrangian (EL) systems with unknown parameters while economizing communication resources. First, to tackle the optimal consensus issue for multiple EL systems with unavailable model parameters, a novel auxiliary system is presented as a reference model that can reach the control objective as well as optimize global cost at the predefined time. Second, a predefined-time tracking controller adopting the time-base generator method is developed to track the auxiliary system. It is a new attempt to introduce a predefined-time convergence mechanism into multiple EL systems with unavailable parameters. Third, a dynamic event triggering mechanism is devised, with triggering thresholds governed by internal dynamic variables. In addition, it effectively eliminates the occurrence of Zeno behavior, thereby illustrating the feasibility of the designed triggering method. Finally, the simulation shows the validity and superiority of the proposed distributed scheme.

This article addresses the consensus control problem for a specific class of discrete-time stochastic multiagent systems (MASs). When transmitting the local measurement output to the local controller and neighboring agents, a multiple description coding (MDC) scheme is introduced to reduce the communication burden and enhance data transmission reliability in a resource-constrained environment. The MDC scheme encodes each signal into two descriptions, which are then transmitted through separate channels, and decoding schemes are employed to address the different characteristics of the arrival of the two descriptions, ensuring the boundedness of the decoding error. The proposed consensus control scheme uses the relative decoded measurement errors between local agents and their neighbors. The aim is to design an output-feedback control scheme that ensures the error dynamics of the controlled MAS reach exponentially mean-square boundedness. Sufficient conditions are established for the existence of the controllers through stochastic analysis techniques, and the desired controller gains are parameterized using the feasibility of certain matrix inequalities. The effectiveness of the proposed coding-decoding-based consensus control scheme is verified through a numerical simulation.

Effective boundary scenario generation methods are critical for dynamic target tracking when evaluating the performance of unmanned aerial vehicles. However, traditional methods primarily rely on exhaustive testing or random sampling and often assume that different contributing factors are independent. This results in the ineffective generation of boundary scenarios and a lack of realism. In this study, a novel high-dimensional dynamic characteristics-based boundary test scenario generation method is proposed using reinforcement learning (RL). Boundary test scenarios can be explored more purposive, by designing an appropriate reward function. First, an innovative scenario modeling method is developed to model the influence of environment, occlusion, and other interference. A joint distribution model of the key correlation factors, such as light intensity and clouds, is also established. This ensures scenario authenticity and test accuracy. Subsequently, a high-dimensional dynamic spatial Markov decision process (HDDS-MDP) model is constructed to facilitate the generation of boundary scenarios based on the scenario modeling. Ultimately, RL is employed to solve the HDDS-MDP model and generate boundary test scenario, thereby substantially improves the effectiveness of boundary test scenario generation. The simulation results indicate that the boundary test scenario generation effectiveness is improved by 91% over random sampling methods.

Mobile edge computing (MEC) plays a pivotal role in the Internet of Vehicles and the Internet of Things. Edge server deployment is the initial step in establishing edge computing systems, which impact the overall system performance significantly. Besides, the performance of an edge computing system is also contingent upon the type of service deployed on servers, in the case of the same server deployment, different deployment of services will bring different profits. Most current studies concentrate solely on the former aspect, neglecting the optimization of service deployment in MEC system. In this article, we proposed a two-step method KPSOP for edge server and edge service deployment, aiming to reduce time delay, balance load, and improve the profit of MEC system, and KPSOP includes clustering algorithm and heuristic algorithm. We considered the location distribution of base stations, the task requests of vehicle users, the resource limitations of edge servers, etc. First, the edge server deployment was completed with the goal of minimizing time delay and load balancing. Second, the service deployment was completed with the goal of maximizing edge server profit. The experiments were based on real world base station information. The simulation results validate that our algorithm is more stable and converges faster. In addition, compared to other algorithms, it performs better in load balance and increasing profit.