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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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Deep Auto-encoder Observer Multiple-Model Fast Aircraft Actuator Fault Diagnosis Algorithm
Jun Ma,Wen-han Dong,Shihong Ni,Wujie Xie 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.4
In the extended multiple model adaptive estimation fault diagnosis algorithm, the extended Kalman filterhas theoretical limitations, and the establishment of accurate aircraft mathematical model is almost impossible. Meanwhile, there is no automatic method to optimally select the node number of deep neural network hiddenlayer. In this paper, a deep auto-encoder observer multiple-model fault diagnosis algorithm for aircraft actuatorfault is proposed. Based on the empirical formula of the basic auto-encoder hidden layer node number selection(three layered neural network), the recursive formula for deep auto-encoder hidden layer node number selectionare proposed. The deep auto-encoder observers for no-fault and different actuator faults are trained to observethe system state. Combined with multiple model adaptive estimation, the deep auto-encoder observer overcomesthe theoretical limitation of extended Kalman filter, and avoided the calculation of the nonlinear system Jacobianmatrix. The simulation results show that hidden layer node number selection recursive formula is useful. The faultdiagnosis algorithm is more efficient and has better performance compared to the standard methods.
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