Suffering from complex sideslip angles, path following control of sailboat becomes significantly challenging. In this article, double finite-time observers-based line-of-sight guidance and finite-time control (DFLOS-FC) scheme is presented for path following of sailboat. Double finite-time sideslip observers (DFSO) are employed to observe the time-varying sideslip angle caused by external disturbances, which improves the accuracy of line-of-sight guidance. To avoid differential explosion problem caused by virtual control law, we designed a finite-time filter. The finite-time disturbance observer (FDO) is designed to accurately observe unknown external disturbances, which enables the controller to have excellent tracking accuracy and precise disturbance rejection. Considering the rotation limit of actuator rudder angle, we limit the rudder angle. The finite-time stability of the integrated guidance and control system is strictly guaranteed by Lyapunov method. Finally, the effectiveness of this method is verified by simulation and comparison with the traditional backstepping method.
In this paper, a novel disturbance observer-based fixed-time formation control method is proposed for autono-mous underwater vehicles with actuator faults, model uncertainties and unknown disturbances. Firstly, a fast fixed-time stable system is improved, which has a faster convergence rate than existing stable system. Then, the leader-follower formation control strategy is combined with the path planning strategy based on the artificial potential field to obtain a collision-free formation configuration. Besides, a fast fixed-time disturbance observer is designed to compensate for unknown composite disturbances, which enhances the robustness of the system. Further, based on the disturbance estimation information and the improved nonsingular fast fixed-time terminal sliding-mode surface, a robust fast fixed-time formation control method is presented, which achieves the desired formation configuration. Finally, simulation and comparison results show the effectiveness of the proposed control strategy.
The reasonable placement of lamps in the classroom will play a vital role in the classroom lighting environment of the classroom, in which the classroom lights and blackboard lights, as the two most important parts of the classroom lighting environment, their importance is self-evident. The illumination level in the classroom can be improved by adjusting the position of the blackboard lights and the classroom lights. At the same time, the influence of the lamps on the illumination level in the classroom is analyzed, and the best parameters are selected by changing the installation position and rotation Angle of the lamps, so as to put forward optimization suggestions for improving the illumination level in the classroom.
What types of shoes that the driver should be wear on driving is under the clear regulation. Non-standard shoe-wearing such as wearing high-heeled shoes, platform shoes, slippers or with bare feet will bring great safety risks and may lead to traffic accidents. According to statistics by traffic department, many traffic accidents are caused by irregular shoe-wearing. Although computer vision has been applied for monitoring driver’s behavior in many aspects, such as driver’s face, eye, and hand, there is still no computer vision-based method or hardware device on the vehicle that to monitor the driver’s shoe-wearing. Therefore, if the driver’s illegal shoe-wearing behavior can be identified before driving, it can play an important role in reducing the incidence of traffic accident. The main difficulties in drivers’ shoe-wearing detection lie in the diversity of shoe types, the variety of foot postures, and the complexity of the detection environment. Therefore, the traditional computer vision method will be high error detection rate in such scenes. In this paper, a deep learning-based method for detecting abnormal shoe-wearing of drivers before driving is proposed. Different models such as SVM, Fast-RCNN, Faster-RCNN, YOLO v2, YOLO v3, YOLO v4 are used to identified drivers’ shoe-wearing. To the best of our knowledge this is the first work that using the computer vision technology for automatic monitoring on drivers’ shoe-wearing. The experimental results show the proposed method can identify the drivers’ shoe wearing effectively and efficiently. It has high application value for improving traffic safety.
People's requirements for office lighting environment are becoming higher and higher, not only for its comfortable and healthy, but also to ensure that people's work efficiency. Aiming at the problem of visual comfort of office light environment, this study carried out subjective and objective evaluation experiment of lighting comfort of office reading environment. This paper studies the coupling influence of lighting elements on paper reading and VDT reading in the office light environment, conducts experiments on the fixed brightness of the screen, and analyzes and discusses the influence of lighting source on visual fatigue degree of reading. In this study, the visual comfort of paper reading and VDT reading was discussed from the perspective of correlation analysis and multiple regression analysis based on the psychophysical data of 30 observers. The experimental results show that visual comfort of paper reading and VDT reading is significantly correlated with illumination and color temperature. The development of this study will provide effective theoretical reference for the design of the current office light environment and has certain guiding significance for the design of light source in various office places.
