Recent efforts have yielded substantial progress in backstepping platoon control for connected and automated vehicles (CAVs). While most existing studies focus on guaranteeing individual vehicle stability and string stability, their deployment in nonlinear vehicle platoons may face challenges from the so-called "butterfly effect." That is, even with guaranteed string stability, potential instantaneous spacing changes may imply unpredictable, uncomfortable fluctuations in vehicular velocity and acceleration. To address this issue, a parallel error-fluctuation suppression control framework is proposed in this work. Specifically, tunable triple-layered error boundaries (i.e., spacing, velocity, and acceleration) are constructed to reactively confine all propagated errors within predefined envelopes. By integrating a Barbalat-lemma-enhanced filtering-compensating mechanism and an adaptive approach based on the approximation capability of radial basis function neural networks (RBFNNs), asymptotic error tracking is realized to proactively suppress potential fluctuations. An adaptive backstepping control approach-integrating proactive and reactive suppression strategies-is then proposed to mitigate the unquantifiable "butterfly effect." Theoretical analysis and simulations demonstrate the validity and superiority of the proposed approach.

Fusarium Head Blight (FHB) poses a substantial threat to global wheat production and food security. Current manual detection methods are inadequate due to their labor-intensive nature and inaccuracies, whereas advancements in Unmanned Aerial Vehicle (UAV) and object detection technologies have improved detection accuracy but still struggle with accurately defining disease boundaries. This study presents MFWNet, a robust UAV-based semantic segmentation model specifically designed for FHB detection. MFWNet innovatively integrates Multi-scale Feature Fusion and Wavelet Convolution, optimizing large kernel convolutions. Key innovations include the Efficient Contextual Convolutional Module (ECCM) for multi-scale feature extraction and fusion and the Feature Enhancement (FE) Module for improved feature representation focused on FHB, thereby improving contextual understanding and boundary precision. Additionally, the implementation of semi-supervised learning for image annotation significantly reduces the need for extensive labeled datasets, which decreases manual annotation efforts and boosts scalability. A wheat FHB dataset consisting of 1,807 pixel-level annotated images was constructed for this purpose. Remarkably, with only 10.15 million parameters, MFWNet achieves a mean Accuracy of 85.78% and a mean Intersection over Union of 73.54%, outperforming nine comparative models in terms of accuracy and efficiency. This method offers an effective solution for large-scale UAV-based FHB detection under real-field conditions, with significant implications for food security.

Effective multiscale medical image segmentation requires simultaneously preserving smooth spatial continuity and accurately delineating high-frequency boundaries, yet pixel-wise decoders often fail to maintain this balance consistently across varying resolutions. We introduce GauSAM, which seamlessly integrates contour‑guided 2D Gaussian probability fields into the Segment Anything Model to address these challenges. In our framework, segmentation masks are parameterized as continuous probability fields of learnable 2D Gaussian primitives, enforcing spatially smooth and structurally consistent. Contourlet transforms extract rich multidirectional frequency information, notably edges and fine textures, which dynamically guide the spatial distribution of Gaussian primitives to substantially improve boundary fidelity in complex structures. The incorporation of these high-frequency contour priors also enriches the expressive capacity of the SAM image encoder. Extensive experiments on diverse 2D medical segmentation tasks confirm that GauSAM consistently delivers robust generalization and state-of-the-art performance with only 1.2M trainable parameters. The official implementation of GauSAM is publicly available at https://github.com/Quinten-Wu504/GauSAM.

In this paper, a resilient distributed model predictive control (DMPC) algorithm is proposed for the networked vehicle platoon system under denial-of-service (DoS) attacks. It is difficult to ensure the system-level stablibity and string stability of the networked vehicle platoon system simultiniously in the presence of DoS attacks. To this end, an optimization problem is established, which is related to the trajectories of the system. To minimize the cost function, the last-step shifting technique is employed and the assumed solution is determined by the local optimal solution. The last optimal solution without the occurrence of DoS attacks is used as the reference trajectory. This reference trajectory is then transmitted to the interconnected vehicles within the system. Demonstrating the stability of the closed-loop platoon system is achieved by using the Lyapunov function, which is designed by the sum of the cost function. String stability of the platoon system, the unique characteristic of platoon systems, is proven through rigorous mathematical derivation. Compared with existing results, the vehicle platoon system under DoS attacks can be converged both the internal and closed-loop stability. Finally, several simulation and experimental results are presented to verify the effectiveness of the proposed method.

This article investigates resilient source seeking problem of second-order multirobot systems (MRSs) under mixed cyberattacks, which consist of misbehaving and Denial-of-Service (DoS) attacks. The misbehaving attacks can cover several types of malicious attacks, such as false data injection, stubborn, and Byzantine, while the network connectivity may be compromised by DoS attacks, potentially resulting in a time-varying and disconnected digraph. To this end, a resilient source seeking algorithm is proposed by designing an auxiliary point for each agent such that the coordination problem is transformed into a point tracking one. A reference velocity is calculated to guide benign robots toward the source, leveraging their historically optimal positions with the highest signal strength. This ensures the auxiliary points converge to the source, clustering benign robots nearby. When DoS attacks occur on some edges, the latest sampling data acquired before the attacks is used to hold the control signals for the robots. Then, sufficient conditions are established through rigorous stability analysis. In comparison to existing methods, the proposed approach extends the safe-kernel-based resilient consensus algorithms to a resilient source seeking algorithm for a general discrete-time second-order dynamics, while also can withstand a mixed cyberattack comprising both misbehaving and DoS attacks. Finally, simulation and experimental results are presented to validate the efficacy of the proposed algorithm.

This article focuses on the safety flocking control problem of networked Lagrangian systems under event-triggered communication. To avoid collision and preserve connectivity, the concept of safety domain and safety vector is presented. Using these definitions, a novel potential function is designed to guarantee the interagent distance to be limited in an exact range during the flocking process. In addition, a fully distributed dynamic event-triggered scheme is proposed to schedule the communication sources. Both the controller and the event-triggered scheme operate without requiring any global information about the entire topology. Also, Zeno behavior is eradicated in the proposed event-triggered communication scheme. Finally, the effectiveness of the proposed method is shown by case studies.

In this article, the resilient coordination problem of networked Lagrangian systems with adversarial agents is considered. A novel algorithm called norm-based resilient decision algorithm is proposed to exclude the impact of adversarial agents. To ensure the coordination of networked Lagrangian systems, the maximum number of adversarial agents related to the robustness of the communication network is given. Under the proposed resilient consensus algorithm, secure coordination is guaranteed under adversarial agents. Then, the proposed resilient controller is extended to static formation scenarios. Finally, the effectiveness of the proposed method is demonstrated through case studies and experiments. Compared to the existing results, the proposed algorithm can reduce computing resources by designing auxiliary vectors and converting them into scalars to remove extreme values.

In this paper, the string stable platoon control problem of discrete-time networked vehicle systems is considered by using distributed model predictive control (MPC) based method. An optimization problem is established to minimize the cost function associated to the system trajectories. The last-step shifting method is applied to set the local optimal solution as the assumed solution and send it to the neighbor vehicles. By using the sum of the cost function as Lyapunov function, the stability of the closed-loop platoon system is studied. Comparing with existing results, the string stability, which is the unique characteristics of the platoon system, is guaranteed under the bidirectional-based structure as well as the predecessor-follower-based information flow structure. Finally, several simulations are presented to demonstrate the effectiveness of the proposed algorithms.

In this article, we consider the resilient consensus control problem of multiple Euler–Lagrange systems in the presence of external disturbances and denial-of-service (DoS) attacks, which have bounded duration and frequency. Unlike the most existing methods for linear multiagent systems, the strong nonlinearity, and unknown disturbances of multiple Euler–Lagrange systems make the resilient control problem under DoS attacks be more challenging and still not well explored. To this end, an adaptive control scheme is first proposed with a robust sliding mode-like term to handle the unknown bounded external disturbances. In addition, an auxiliary system is designed by using the sampled neighboring information to achieve resilience to DoS attacks. Sufficient conditions are presented by using Lyapunov and small-gain approaches. Finally, simulation results are given to verify the effectiveness of the proposed resilient consensus control approach.

In this article, the predefined-time cooperative platoon control problem with constrained communication range for uncertain networked aerial vehicles is considered. The uncertain networked aerial vehicles are subjected to external disturbance and parameter uncertainties. A novel adaptive sliding-mode disturbance observer is first designed for single uncertain networked aerial vehicle with an radial basis function of neural network estimator to guarantee the control performance. By embedding a dynamic control gain associated with the predefined convergence time, the proposed disturbance observer is proved to be uniformly ultimate boundedness stable by the time transformation approach. Then the dynamic gain technology is combined with the prescribed performance control to design the platoon controllers for uncertain networked aerial vehicles. With the proposed disturbance observer and the distribute controller, the platoon can be achieved within the predefined time. The proposed approach can simultaneously guarantee the system stabile without any initial conditions and system parameters. Finally, some simulation experiments are given to verify the effectiveness of the proposed protocols.

In this article, we consider the formation tracking problem of nonholonomic multiagent systems only using relative bearing measurements between the agents. Such a practical and important yet challenging issue has been taken into limited consideration by existing approaches, which usually requires additional measurements such as relative positions. The contributions of this article are two-fold. First, a fully distributed reference velocity estimator is proposed. Under the proposed adaptive estimator, each agent can estimate the time-varying reference velocity asymptotically. Second, an input-to-state stable controller is designed according to the bearing rigid theory. Under the proposed controller, the formation with bearing-only constraints can be achieved. Finally, the proposed scheme is demonstrated and its effectiveness is verified by presenting some simulation and experimental tests.

A wide range of cyber–physical systems can be modeled as Euler–Lagrange dynamics, whose inherent nonlinearities bring additional difficulties in the design and analysis of controllers for such systems. The objective of this article is to solve the jamming-resilient synchronization control problem of networked Lagrangian systems subject to unknown external disturbances, with the quantized sampling data. Under a jamming attack, a normal communication channel will be blocked with unknown frequencies and intervals. To achieve jamming attack resilience, a robust adaptive controller is proposed. Then a novel auxiliary system is designed for each subsystem over a directed network. By using the quantized sampling data from each subsystem and its neighbors, the jamming attack can be handled by an embedded single-integral subsystem. Sufficient conditions that are independent of the attack parameters are derived to guarantee the global stability of the closed-loop system. Compared with existing jamming-resilient methods, the proposed approach is concise and resilient to any jamming attacks with bounded frequencies and durations.

In this paper, we consider the optimal trajectory tracking control problem for a cyber-physical marine surface vessel system with unknown dynamics by using a reinforcement learning (RL) algorithm. The main contributions of the paper are threefold: (1) The dynamics of the marine vessel system considered in this paper is partially unknown. (2) The actor-critic-identifier (ACI) architecture based on fuzzy logical system (FLS) approximation is designed to handle the optimal tracking control problem; (3) The same Bellman error is chosen to update the actor and critic FLS weights at the same time. To be more specific, by using FLS approximating the unknown optimal value function, optimal policy, and partial unknown dynamic, a reinforcement learning algorithm is used to solve the optimal tracking problem of the marine surface vessel system. By designing a fuzzy identifier, the actor-critic weights are adaptively updated simultaneously. The rigorous proof is given to verify the correctness of the algorithm.