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- 저자 : Dong Wenhan (검색결과 3 건)
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Kaibiao Yang,Wenhan Dong,Yingyi Tong,Lei He 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.10
This paper proposes a solution to the external disturbance, uncertain aerodynamic parameters and inter formation collision in coordinated flight of leader-follower quadrotor UAV formation. We present a trajectory tracking control method for UAV formation with preset performance function constraints. We first model the quadrotor UAV system with a dual closed-loop system with position outer loop and attitude inner loop. The performance constraint function transforms the output constraint problem into an unconstrained problem through error transformation. We show that the uncertain aerodynamic parameters and the external disturbance can be estimated by proper design of an exponential disturbance observer. To solve the trajectory tracking problem of the Leader UAV, we introduce a backstepping adaptive control (BC) based on the preset performance. Furthermore, to overcome the issue of the collision between aircrafts in the formation flying, the formation sliding mode control (SMC) based on the power approach rate is proposed. Such a mechanism ensures that the UAVs track their respective reference trajectories quickly, so as to achieve the desired formation. The effectiveness of the control strategy is investigated by simulation. In addition, the open-source autopilot as well as the formation control airborne computer, wireless communication links and used data packet structure are presented, and the designed control law is transplanted into the Pixhawk flight control processor. The feasibility of the control strategy is demonstrated by the quadrotor UAV formation experimental platform based on the Robot Operating System (ROS).
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Hu Duoxiu,Dong Wenhan,Xie Wujie,He Lei 한국항공우주학회 2022 International Journal of Aeronautical and Space Sc Vol.23 No.2
A Markov decision process model with two stages of long-distance autonomous guidance and short-distance autonomous tracking of obstacle avoidance was developed in this study, aiming to address the performance problem of multi-rotor unmanned aerial vehicles (UAV) to ground dynamic target. On this basis, an improved proximal policy optimization (PPO) algorithm is proposed. The proposed algorithm uses long short-term memory (LSTM) network to calculate reward values, update network parameters and perform adaptive optimization iterations through status information, such as the real-time position relationship between the UAV and the target, taking into account the time-sequential data received from the UAV and the environmental context information. Finally, experiment with simulation testing was performed on platform based robot control system species. The results showed that the method proposed in this paper is able to safely and effectively realize autonomous maneuvering during the entire process of the reconnaissance mission. Compared with the traditional PPO algorithm, the introduction of LSTM neural network shortened the model training time, considerably improved the efficiency of tracking and avoiding obstacles, as well as further strengthened the robustness, accuracy, and real-time ability of the algorithm.
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Adaptive Sliding Mode Control of a Transport Aircraft for Heavyweight Airdrop
Ri Liu,Xiuxia Sun,Wenhan Dong,Guangzhi Xu 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
This paper investigates the problem of designing a novel sliding mode controller of a transport aircraft for airdrop modes in the presence of bounded nonlinear uncertainty and actuator fault without the prior knowledge of the bounds. On the basis of feedback linearization of the aircraft-cargo dynamic model, an autopilot inner-loop which combines sliding mode control with adaptive function approximation is developed. The complex nonlinear uncertainty of the model is factorized into a known matrix and an uncertainty function. An adaptive approximation approach is used to estimate the function, and it overcomes the conservation drawback of the sliding mode control that relies on the bounds on the uncertainty/actuator fault. Notably, the adaptation law formed using the projection operator can bound the estimated function, and this avoids singularity of the control signal. Simulations verify the good performance of the control system, which can satisfy the airdrop mission performance indexes well, even in the presence of ±20% aerodynamic coefficients uncertainty and 20% actuator fault.
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