Due to the nonlinear and asymmetric input constraints of the fixed-wing UAVs, it is a challenging task to design controllers for the fixed-wing UAV formation control. Distance-based formation control does not require global positions as well as the alignment of coordinates, which brings in great convenience for designing a distributed control law. Motivated by the facts mentioned above, in this paper, the problem of distance-based formation of fixed-wing UAVs with input constraints is studied. A low-gain formation controller, which is a generalized gradient controller of the potential function, is proposed. The desired formation can be achieved by the designed controller under the input constraints of the fixed-wing UAVs with proven stability. Finally, the effectiveness of the proposed method is verified by the numerical simulation and the semi-physical simulation.
This paper proposes a new algorithm to solve the control problem for a special class of distance-based directed formations, namely directed-triangulated Laman graphs. The central idea of the algorithm is to construct a virtual point for the agents who have more than two neighbors by employing the information of the desired formation. Compared with the existing methods, the proposed algorithm can make the distance error between the agents converge faster and the path consumption is less. Furthermore, the proposed algorithm is modified to be operable for the small fixed-wing UAV model with nonholonomic and input constraints. Finally, the effectiveness of the proposed method is verified by a series of simulation experiments.
Purpose
This paper aims to realize a fully distributed multi-UAV collision detection and avoidance based on deep reinforcement learning (DRL). To deal with the problem of low sample efficiency in DRL and speed up the training. To improve the applicability and reliability of the DRL-based approach in multi-UAV control problems.
Design/methodology/approach
In this paper, a fully distributed collision detection and avoidance approach for multi-UAV based on DRL is proposed. A method that integrates human experience into policy training via a human experience-based adviser is proposed. The authors propose a hybrid control method which combines the learning-based policy with traditional model-based control. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the approach.
Findings
A fully distributed multi-UAV collision detection and avoidance method based on DRL is realized. The reward curve shows that the training process when integrating human experience is significantly accelerated and the mean episode reward is higher than the pure DRL method. The experimental results show that the DRL method with human experience integration has a significant improvement than the pure DRL method for multi-UAV collision detection and avoidance. Moreover, the safer flight brought by the hybrid control method has also been validated.
Originality/value
The fully distributed architecture is suitable for large-scale unmanned aerial vehicle (UAV) swarms and real applications. The DRL method with human experience integration has significantly accelerated the training compared to the pure DRL method. The proposed hybrid control strategy makes up for the shortcomings of two-dimensional light detection and ranging and other puzzles in applications.
In this paper, we propose an Extended Kalman Filtering with phase noise reconstruction (EKF-PC) scheme to enhance the carrier recovery capability for probabilistic shaping of coherent optical communication systems with various shaping factors. We first investigate the weights of the shaping factor and the noise rejection window length of EKF-PC for PS-64QAM at a fixed signal-to-noise ratio (SNR). After that, we jointly optimize the shaping factor and the noise rejection window length to obtain the maximum achievable information rate at a variety of SNRs. Then, we numerically analyze the carrier recovery performance of the EKF-PC for different linewidths and SF conditions. Finally, we conduct simulation experiments to compare EKF-PC, PCPE, and other currently available Kalman CPE algorithms with the SFs of 0.02, 0.025, 0.03, and 0.035 under back-to-back (B2B) scenarios. The experimental results show that EKF-PC obtains an average SNR improvement of 0.13–0.5 dB compared to PCPE and an average performance improvement of 0.5–1 dB compared to other Kalman algorithms.
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