Attitude and orbit coupled control and simulation of spacecraft formation by using intelligent adaptive control based on characteristic model
Regarding to the relative attitude and orbit of spacecraft formation flying, a nonlinear dynamic model of six degree of freedom coupled with attitude and orbits established, with the help of characteristic model, this paper mainly analyzes the factors of the spatial perturbation and multiple disturbance torques, a kind of intelligent adaptive controller is designed based on the combination of RBF neural network and the golden section adaptive control. Later, the simulation platform Jade is used to simulate and analyze the Multi-Agent system of spacecraft formation. The results show that the spacecraft formation can run along in the ideal orbit, the effect of the control system has good robustness, fault tolerance and intelligence.
Soft-shell Shrimp Recognition Based on an Improved AlexNet for Quality Evaluations
Abstract Shrimp quality evaluations fulfill an essential role in producing high-value shrimp products. The presence of soft-shell shrimp deteriorates the quality of shrimp products. The biggest challenge in preventing this is the similarity in appearance of soft-shell (s-shrimp) and sound (o-shrimp) shrimp from an imaging perspective. This similarity imposes significant limitations on distinguishing them with traditional machine vision methods. To circumvent this problem, a novel method based on deep convolutional neural networks (Deep-ShrimpNet) is proposed. Initially, several image processing steps were performed to normalize the shrimp image. Furthermore, four critical hyper-parameters (i.e., batch-size, dropout ratio, learning rate and number (size) of local receptive fields) were optimized by a comparative analysis. Additionally, the self-learned combined features in each convolutional layer were visualized to explore the internal mechanism of Deep-ShrimpNet. To obtain the efficient strategy, an ablation study was also performed by removing layers of the CNNs. Finally, the superiority of the proposed algorithm was verified through a comparison with other sophisticated CNNs. In a test dataset, Deep-ShrimpNet achieved a mean accuracy precision (mAP) of 0.972 and modeling time of 0.54 h. The robust performance of the proposed method across the shrimp dataset indicates that Deep-ShrimpNet is promising for on-line shrimp classification and quality measurement.
Position and Attitude Control of Deep-Space Spacecraft Formation Flying Via Virtual Structure and θ-D Technique
Control of deep-space spacecraft formation flying is investigated in this paper using the virtual structure approach and the θ-D suboptimal control technique. The circular restricted three-body problem with the Sun and the Earth as the two primaries is utilized as a framework for study and a two-satellite formation flying scheme is considered. The virtual structure is stationkept in a nominal orbit around the L 2 libration point. A maneuver mode of formation flying is then considered. Each spacecraft is required to maneuver to a new position and the formation line of sight is required to rotate to a desired orientation to acquire new science targets. During the rotation, the formation needs to be maintained and each spacecraft's attitude must align with the rotating formation orientation. The basic strategy is based on a "virtual structure" topology. A nonlinear model is developed that describes the relative formation dynamics. This highly nonlinear position and attitude control problem is solved by employing a recently developed nonlinear control approach, called the 0-D technique. This method is based on an approximate solution to the Hamilton-Jacobi-Bellman equation and yields a closed-form suboptimal feedback solution. The controller is designed such that the relative position error of the formation is maintained within 1 cm accuracy.