Article
Engineering, Aerospace
Yuan Zhang, Ran Zhang, Huifeng Li
Summary: This paper focuses on the mixed-integer trajectory optimization problem of avoiding no-fly zones for a hypersonic vehicle. It integrates the discrete path decision and continuous trajectory optimization to form a mixed-integer optimal control problem (MIOCP). An iterative mixed-integer convex programming (IMICP) algorithm is customized to solve the MIOCP, which improves the global performance by addressing artificial infeasibility. Simulation results demonstrate that this approach is independent of the initial guess and outperforms existing methods.
Article
Automation & Control Systems
Lu Lv, Xinggao Liu, Long Xiao, Weihua Ma, Zhenqiang Qi, Song Ye, Guoqiang Xu, Zongzhun Zheng, Sen Wang, Zeyin Zhang
Summary: A novel optimal control approach is proposed in this study, where waypoints are transformed into optimization parameters and an adaptive gradient analysis with mesh refinement is introduced for a non-uniform pseudospectral method, achieving better accuracy performance and saving computational time. The research results validate the effectiveness and time saving benefits of these proposed methods.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Engineering, Aerospace
Yu Wu, Jianing Deng, Leilei Li, Xichao Su, Liyang Lin
Summary: The hybrid PSO-GPM algorithm proposed in this paper combines PSO and GPM to optimize the reentry trajectory of hypersonic vehicles, improving the efficiency and precision of the solution with a combination of inner and outer loops. It can also enhance the safety level of flight compared to using PSO or GPM separately.
AEROSPACE SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Aerospace
Ya Su, Ying Dai, Yi Liu
Summary: The paper introduces a hybrid algorithm combining the hyper-heuristic whale optimization algorithm and the Gauss pseudospectral method for automatic reentry trajectory optimization without user-specified initial guesses. The proposed algorithm shows promising results in addressing RLV reentry trajectory optimization problems.
AEROSPACE SCIENCE AND TECHNOLOGY
(2021)
Article
Mathematical & Computational Biology
Wenyang Gan, Lixia Su, Zhenzhong Chu
Summary: This study investigates a fast optimization method for trajectory optimization of obstacle-avoidance navigation of autonomous underwater vehicles (AUVs) using the Gauss pseudospectral method (GPM) and particle swarm optimization (PSO). A multi-constraint trajectory planning model is established and the optimization problem is solved by converting it into a non-linear programming problem and applying the sequential quadratic programming (SQP) algorithm. To improve the performance of the SQP algorithm, the PSO pre-planning method is proposed and it significantly accelerates the convergence speed of the optimal solution. The results show that the PSO-GPM method improves computational efficiency by 82.3% and 88.6% compared to linear interpolation and cubic spline interpolation methods, respectively, demonstrating its effectiveness in solving the trajectory optimization problem.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2023)
Article
Thermodynamics
Muyin Tian, Zuojun Shen
Summary: In this study, a hybrid method is proposed to efficiently generate optimal trajectories for air-breathing hypersonic vehicles, while dealing with uncertainties in no-fly zones using a chance-constrained approach. The effectiveness of this method is demonstrated through numerical simulations.
ADVANCES IN MECHANICAL ENGINEERING
(2022)
Article
Engineering, Aerospace
Tengfei Zhang, Hua Su, Chunlin Gong
Summary: The study introduces a SCP algorithm based on RPD, which divides the iteration process into three stages and applies constraint relaxation technique, adaptive updating of discretized points, and regularization technique to improve the accuracy and efficiency of reentry trajectory optimization problems.
AEROSPACE SCIENCE AND TECHNOLOGY
(2022)
Article
Automation & Control Systems
Tuo Han, Qinglei Hu, Hyo-Sang Shin, Antonios Tsourdos, Ming Xin
Summary: This paper proposes a passive fault tolerant control scheme for the full reentry trajectory tracking of a hypersonic vehicle. The scheme utilizes attitude error dynamics and a multivariable twisting controller to achieve precise tracking, and further optimizes the system by reducing model dependency and system uncertainties through an incremental twisting fault tolerant controller.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Pei Pei, Shipeng Fan, Wei Wang, Defu Lin
Summary: This article proposes a modified sequential convex programming (MSCP) method to solve a highly nonlinear trajectory optimization problem for reentry vehicles. By decoupling the dynamics and convexifying the objective function, the continuous-time optimal problem is converted to an equivalent finite-dimensional sequential convex programming problem to ensure optimality of the solution and maintain feasibility with a compensation term.
Article
Engineering, Aerospace
Yudong Hu, Changsheng Gao, Junlong LI, Wuxing Jing, Wenxue Chen
Summary: In this study, a novel adaptive lateral reentry guidance algorithm is proposed to address the complex distributed no-fly zones avoidance problems. The improved artificial potential field method and heading corridor are utilized to effectively determine the reference heading angle and increase sensitivity to the threat changes of the no-fly zones.
CHINESE JOURNAL OF AERONAUTICS
(2022)
Article
Engineering, Aerospace
Jianying Wang, Yuanpei Wu, Ming Liu, Ming Yang, Haizhao Liang
Summary: This paper proposes a real-time trajectory optimization method based on a deep neural network for hypersonic vehicles, which can quickly generate optimal trajectories with high computational efficiency and reliability. Compared to traditional methods, it has better generalization capability and meets the demands of online real-time trajectory optimization.
Article
Automation & Control Systems
Tiancai Wu, Honglun Wang, Yiheng Liu, Tianren Li, Yue Yu
Summary: In order to solve the problem of no-fly zone avoidance for hypersonic reentry vehicles, a learning-based avoidance guidance framework is proposed. The framework efficiently solves the reference heading angle determination problem and incorporates various mechanisms to guide the vehicle towards the target while avoiding no-fly zones in the gliding phase. The proposed algorithm is further optimized in real-time using a learning-based online mechanism.
Article
Engineering, Aerospace
Shengnan Fu, Tianyu Lu, Jian Yin, Qunli Xia
Summary: The proposed algorithm estimates landing footprints based on the drag acceleration-energy profile for an entry hypersonic vehicle, using a drag acceleration tracking law and flying strategies. It can rapidly calculate landing footprints for different situations and shows good adaptability.
INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Ya Su, Ying Dai, Yi Liu
Summary: The paper introduces a hybrid parallel Harris hawks optimization (HPHHO) algorithm to address reentry trajectory optimization for reusable launch vehicle (RLV). By utilizing oppositional learning, smoothing technique, and parallel optimization mechanism, HPHHO aims to enhance efficiency and robustness, showing better performance metrics, convergence rate, and stability.
Article
Engineering, Electrical & Electronic
Wanli Wen, Kun Luo, Lanhui Liu, Yi Zhang, Yunjian Jia
Summary: This paper explores the use of unmanned aerial vehicles (UAVs) for item delivery in the presence of multiple no-fly zones. The authors mathematically characterize the item delivery scenario and formulate a problem of item value maximization to optimize the UAV trajectory and item pick-up design. They propose a low-complexity algorithm based on the penalty convex-concave procedure method and demonstrate its superiority through numerical results.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Aerospace
Ningjun Liu, Zhihao Cai, Jiang Zhao, Yingxun Wang
CHINESE JOURNAL OF AERONAUTICS
(2020)
Article
Engineering, Aerospace
Zhihao Cai, Longhong Wang, Jiang Zhao, Kun Wu, Yingxun Wang
CHINESE JOURNAL OF AERONAUTICS
(2020)
Article
Engineering, Aerospace
Yingxun Wang, Tian Zhang, Zhihao Cai, Jiang Zhao, Kun Wu
CHINESE JOURNAL OF AERONAUTICS
(2020)
Article
Engineering, Aerospace
Ningjun Liu, Zhihao Cai, Yingxun Wang, Jiang Zhao
Summary: By designing a three-stage tiltrotor landing maneuver and utilizing the ESO control module, the landing time of the tiltrotor is shortened, and stable flight is achieved at low altitudes. Flight test results demonstrate the effectiveness of the desired fast transition maneuver.
CHINESE JOURNAL OF AERONAUTICS
(2021)
Article
Engineering, Aerospace
Zexin Wang, Jiang Zhao, Zhihao Cai, Yingxun Wang, Ningjun Liu
Summary: This paper presents a robust attitude control scheme for quadrotors, which involves an attitude controller and an angular acceleration controller based on dynamic inversion. By incorporating an onboard actuator model, the proposed controller demonstrates improved robustness against model mismatch and disturbances, outperforming the traditional PID controller in numerical simulations and flight tests.
CHINESE JOURNAL OF AERONAUTICS
(2021)
Article
Engineering, Aerospace
Xingling Shao, Yi Shi, Wendong Zhang, Jiang Zhao
Summary: A prescribed fast tracking control scheme is proposed for Flexible Air breathing Hypersonic Vehicles (FAHV) to ensure convergence of tracking errors with reduced oscillations. Switching event-triggered mechanisms are utilized to avoid excessive resource occupation and guarantee tracking accuracy, while Uncertainty and Disturbance Estimators (UDE) and Sigmoid function-based Tracking Differentiators (STD) are employed for disturbance estimation with low computational complexity. Robust control laws are designed to compensate for sampling errors induced by event-triggered conditions and eliminate Zeno phenomena, demonstrating the effectiveness of the proposed scheme.
CHINESE JOURNAL OF AERONAUTICS
(2021)
Article
Engineering, Aerospace
Jiang Zhao, Jiaming Sun, Zhihao Cai, Yingxun Wang, Kun Wu
Summary: This paper proposes a new distributed coordinated control scheme based on heterogeneous roles for Unmanned Aerial Vehicle (UAV) swarm to achieve formation control. The framework of the distributed coordinated control scheme is designed on the basis of Distributed Model Predictive Control (DMPC). The effect of heterogeneous roles including leader, coordinator and follower is discussed, and role-based cost functions are developed to improve the performance of coordinated control for UAV swarm. Coordination strategies are proposed for UAVs with different roles to achieve swarm conflict resolution.
CHINESE JOURNAL OF AERONAUTICS
(2022)
Article
Automation & Control Systems
Zhihao Cai, Zexin Wang, Jiang Zhao, Yingxun Wang
Summary: This paper explores the equivalence between the linear active disturbance rejection control (LADRC) and incremental nonlinear dynamic inversion (INDI) controllers. By verifying the equivalence, an actuator model is integrated into the LADRC to improve its performance on systems with non-negligible actuator dynamics.
Article
Chemistry, Multidisciplinary
Jiang Zhao, Jiaming Sun, Zhihao Cai, Longhong Wang, Yingxun Wang
Summary: The study introduced a new end-to-end autonomous control method for UAVs using deep reinforcement learning, simplifying the modular design in traditional control pipelines. Training with model-free algorithms, the method proved feasible for perception-based autonomous control in UAV landing scenarios.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Mingjun Li, Zhihao Cai, Jiang Zhao, Jinyan Wang, Yingxun Wang
Summary: In this paper, a neural network controller is designed and trained for quadrotor attitude control to expand its application in complex and challenging tasks. The controller enables the quadrotor to reject strong disturbance and achieve high dynamic control.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Chemistry, Analytical
Mingjun Li, Zhihao Cai, Jiang Zhao, Yibo Wang, Yingxun Wang, Kelin Lu
Summary: This paper investigates the autonomous tracking of moving targets by unmanned aerial vehicles using only an airborne camera sensor, proposing an integrated controller framework based on multi-neural-network modules (MNNMs). Using deep learning and reinforcement learning methods to train the integrated controller resulted in quicker and more efficient training compared to end-to-end controllers. Flight tests showed that the integrated controller trained in simulation could successfully track moving targets in a realistic environment, outperforming other control modes.
Article
Chemistry, Analytical
Zexin Wang, Yingxun Wang, Zhihao Cai, Jiang Zhao, Ningjun Liu, Yanqi Zhao
Summary: This paper proposes a unified attitude controller based on the modified linear active disturbance rejection control (LADRC) for a dual-tiltrotor unmanned aerial vehicle (UAV) with cyclic pitch. The proposed control algorithm shows higher robustness against model mismatch and utilizes onboard sensors for state feedback instead of the mathematical model. By integrating an actuator model, the modified LADRC improves stability and performance. The controller replaces gain-scheduling or control logic switching, simplifying the design approach for different flight modes.
Article
Engineering, Multidisciplinary
Jiang Zhao, Han Liu, Jiaming Sun, Kun Wu, Zhihao Cai, Yan Ma, Yingxun Wang
Summary: This paper proposes a deep reinforcement learning-based end-to-end control method for UAV dynamic target tracking, which addresses the challenges posed by target motion uncertainty, limited perception ability, and constrained control. The proposed method simplifies the traditional modularization paradigm and trains the policy network using neural network architecture, reward functions, and SAC-based speed command perception algorithm. Numerical simulation results demonstrate the feasibility of the proposed method in tracking dynamically changing targets.
Article
Engineering, Multidisciplinary
Yingxun Wang, Bo Shao, Chongchong Zhang, Jiang Zhao, Zhihao Cai
Summary: This paper presents a new range and event-based visual-inertial odometry (REVIO) method to address the problems of motion drift and motion blur in UAVs and robotics. By fusing events and distances, REVIO provides more accurate localization performance in fast-motion and high-dynamic scenes.
Article
Engineering, Multidisciplinary
Jiang Zhao, Shilong Ji, Zhihao Cai, Yiwen Zeng, Yingxun Wang
Summary: This paper proposes a new solution for moving object detection and tracking using event cameras. It combines event frames and standard frames for object detection, utilizes improved filters for object tracking, and applies similar triangle theory for distance measurement. Experimental results demonstrate the effectiveness of these methods.
