Yan-Jun Liu

For a class of heterogeneous multi‐agent systems (MASs) with distinct dynamical orders, unknown parameters, and actuator faults, a distributed dynamic event‐triggered (DET) adaptive fault‐tolerant consensus control strategy is proposed. First, a distributed reference system with order‐matching is constructed for each agent to handle the heterogeneity in orders; Second, a trigger mechanism with dynamic thresholds is designed to ensure output consistency while substantially reducing the communication frequency; then, by integrating adaptive backstepping technology with distributed fault observers, unknown parameters and actuator faults are estimated and compensated online; finally, a composite Lyapunov function is constructed to prove that all signals of the closed‐loop system are globally uniformly bounded, the outputs achieve asymptotic consensus, and no Zeno behavior exists. Simulation results demonstrate that the proposed method can still achieve consensus in the presence of actuator faults, and significantly reduces the communication burden compared with static event triggering under the same performance requirements.

In this paper, we present a controller approach to solve the problem of bearing‐constrained autonomous aerial vehicle (AAV) reaching a target with predefined accuracy, also avoiding multiple obstacles and coping with possible actuator failure in the process. First, to ensure that AAV with weak perception capabilities still maintains high performance, we propose a bearing‐based AAV localization model to accurately locate targets and obstacles while only obtaining angular information, which effectively solves the problem of maintaining high performance of AAV with limited loading capacity and cost. Second, an obstacle avoidance assistance system is designed in combination with the localization model, so that the AAV can avoid obstacles perfectly even with local perception. Third, we also employ an asymmetric time‐varying Lyapunov barrier function (BLF) to enable the AAV to reach the target with a predefined accuracy, and use neural networks (NNs) to approximate unknown functions in the system while being able to cope with actuator failures. Finally, simulation experiments verify that the AAV can perfectly achieve the control goal in various environments.

Current research on constrained problems primarily focuses on the design of symmetric or fixed piecewise barrier Lyapunov function parameters, whereas constraints in practical systems often exhibit asymmetry and time-varying characteristics. This paper addresses the vibration suppression problem for a Flapping-Wing Robot Aircraft (FWRA) system subject to asymmetric time-varying output constraints. Firstly, the dynamic model of the FWRA system is established based on Partial Differential Equations (PDEs). Secondly, a shifting function is introduced to relax the boundary output constraint conditions at the initial time instant. Given the asymmetry and time-varying nature of constraints in practical systems, this paper designs a piecewise Lyapunov function incorporating the shifting function, aiming to suppress boundary vibrations within an asymmetric open set. Accordingly, a boundary control scheme is proposed. Furthermore, through Lyapunov stability analysis, the uniform boundedness of the closed-loop system is rigorously proven. Finally, numerical simulation results verify the effectiveness of the proposed control method, demonstrating that the output of the FWRA system remains rigorously within the prescribed time-varying boundaries after the constraint activation time.

When performing autonomous exploration in unknown and complex environments, robots rely on high-accuracy real-time environmental maps for reliable navigation. However, existing LiDAR-based simultaneous localization and mapping (SLAM) methods often encounter challenges such as point cloud feature redundancy, motion distortion, and cumulative errors in such scenarios. To address these issues, we propose a LiDAR-inertial SLAM algorithm that integrates point cloud clustering segmentation with Scan Context-based loop closure detection. First, this method constructs an odometry using inertial measurement unit (IMU) preintegration to mitigate motion distortion in the LiDAR point cloud. Second, it achieves efficient and robust feature extraction through point cloud clustering segmentation and curvature analysis. Finally, the Scan Context descriptor is introduced for loop closure detection, and a factor graph is built to globally optimize the LiDAR pose, effectively suppressing cumulative errors. Experimental results show that, compared to mainstream algorithms like tightly-coupled LiDAR inertial odometry via smoothing and mapping (LIOSAM), the proposed method significantly improves both mapping accuracy and efficiency, particularly for robotic exploration in unknown, complex environments.

Autonomous navigation requires real-time trajectory optimization with limited onboard computational resources, where traditional optimization-based methods often impose heavy computational burdens and require extensive parameter tuning. To achieve transparent navigation results and minimize computational burden, this paper introduces an iterative learning planning (ILP) approach for navigation. By dynamically integrating the control layer into the planning layer, ILP can achieve efficient trajectory optimization through functional iterative learning, providing transparent results that enhance navigation efficiency. This approach significantly improves real-time performance, achieving a time complexity of O (k^*n), where k^* represents the number of iterations and n represents the number of waypoints. Simulations and quadcopter experiments are conducted to verify the proposed framework. Results show that ILP significantly improves computational efficiency, ensures safe navigation, and maintains robustness against disturbances, demonstrating its potential as a practical solution for real-world autonomous systems.

空地无人集群通过异步感知构建更精准完备的环境模型, 既能弥补视野盲区、增强通讯抗扰能力, 又能实现性能互补、提高载荷续航水平. 针对非规则空间中异构无人集群协同运行的多约束安全条件, 实现多阶段目标协同轨迹规划与精准稳定控制问题, 提出一种空地跨域动态立体虚拟管廊安全规划与分布式自适应跨域重构协同控制策略. 利用分层架构: 外层位置控制运用向量场算法结合动态燕麦优化算法, 生成基准曲线和动态安全半径, 以此为基础构建滚动空地跨域立体虚拟管廊; 进一步, 在预设无障碍虚拟管廊安全空间域内展开规划, 依据空地无人集群协同运动的相关条件, 设计满足饱和条件下的集群协同速度规划策略, 确保有效避碰, 同时维持空地集群的内聚性, 生成以虚拟领导者为指引的空地无人机/车各自的最优运动轨迹, 且支持编队的自调节. 内层速度控制融合跨域矩阵与分布式模糊观测器, 为提高异质传感器局部不可测情况以及异构系统跨域信息交互能力, 设计了分布式自适应输出反馈控制器并重构了矩阵块型的拉普拉斯矩阵. 有效应对在非规则障碍环境及状态信息不完备情况下实现空地无人集群多约束协同安全轨迹优化与分布式重构智能控制, 保障动态空地跨域协同多阶段运动任务与精准稳定性控制. 最后, 基于Lyapunov理论证明了空地无人集群的稳定性, 并通过仿真算例验证了动态安全管廊下的空地无人集群队形自调节控制与动态路径规划方法的精准有效性.

This paper investigates the nonlinear placement problem for multi-cluster autonomous aerial vehicles (AAVs) formations. The AAVs in each cluster are positioned to achieve the target configuration while minimizing the sum of the squares Euclidean space distances between each center and its corresponding members of the cluster. The problem is reformulated as a time-varying noncooperative game. Firstly, considering the external disturbances of AAVs, a fast terminal sliding mode observer (FTSM) is developed to estimate the disturbance, and the convergence of the observer is proved by the Lyapunov stability theorem. Then, to search Nash equilibrium (NE) for the time-varying non-cooperative game problems, a distributed algorithm based on adaptive connectivity maintenance mechanism and gradient descent method is designed. The algorithm achieves asymptotic convergence for Nash equilibrium, which is proved by means of an iterative approach. Finally, the algorithm's effectiveness is demonstrated through a simulation example.

In this article, an adaptive fuzzy event‐triggered fixed‐time prescribed performance (FTPP) containment control scheme is investigated for switched nonlinear multi‐agent systems (MASs). The containment errors can always be suppressed to the prescribed boundary and converge to the steady‐state performance function within the predesigned time via introducing the FTPP function. Fuzzy logic systems (FLSs) are used to identify the uncertain nonlinear items, and a state observer is designed to estimate the unmeasurable states. By integrating backstepping and command filtering, the issue of “explosion of complexity” is eliminated. To reduce the communication redundancy while ensuring the steady‐state performance, an event‐triggered controller (ETC) is designed based on a common Lyapunov function. The proposed containment control scheme can guarantee that all signals in the closed‐loop system are bounded, the output of each agent converges to the convex hull formed by multiple leaders, the containment error can always fall into the prescribed boundary, and converges to the steady‐state performance function boundary at the predetermined time. Finally, the effectiveness of the proposed control method is verified through a simulation example.

In this article, we investigate the adaptive safety-constrained control problem for quadrotor unmanned aerial vehicle (QUAV) clusters in dense forest environments to achieve adaptive navigation and obstacle avoidance. Compared to traditional methods, obstacle avoidance constraints are introduced for the first time, and the limitations of fixed formations and the need for prior data are eliminated. First, a cluster constraint mechanism is developed to constrain the distance between QUAVs within the cluster and the distance between the QUAV and the desired trajectory. Then, considering the lack of targeted obstacle avoidance constraint mechanisms in previous methods and the extensive prior data required by learning-based approaches, an obstacle constraint model is established to ensure that the QUAV maintains a safe distance from obstacles to avoid collisions. Finally, an adaptive safety control strategy for QUAV clusters is proposed by combining constraint conditions and stability criteria. Under the proposed control strategy, the QUAV clusters can achieve stable, safe, and efficient navigation and obstacle avoidance, and all constraints will always be satisfied. Furthermore, a numerical simulation experiment on a QUAV cluster navigation demonstrates the effectiveness and flexibility of this strategy.