This paper proposes a simplified path-following control method for an Unmanned Surface Vessel (USV) considering towed Unmanned Underwater Vehicles (UUV). For dealing with an effective USV dynamic model, 1st order of the linear system with time delay and gain value are applied rather than applying a non-linear dynamic model, and it is identified with real vessel data from several straight and turning experiments. Then, USV attitude and velocity are controlled by multi-loop Proportional-Derivative (PD) and proportional controller. A USV guidance scheme is derived through a UUV guidance scheme to support autonomous navigation for towed UUV, and combination of cross track and Line of Sight (LOS) guidance is presented for adaptive path following. Finally, to validate the performance of the proposed USV path-following control method with respect to the towed UUV guidance scheme, the results of simulations are presented.
This paper investigates the relationship between the preload level of a ball screw drive and the detected natural frequency of the system in an axial direction. A dynamic model to study the preload variation of the system is derived, and then a preload feature is proposed for extracting preload conditions based on the detected natural frequency of the system. A modified double-nut ball screw drive system with adjustable preload level is constructed. This is for the purpose of experimental verification. An accelerometer is attached to the ball screw nuts of the drive system to acquire vibration signals. The signals are analyzed to obtain the natural frequency of the ball screw drive system in an axial direction. By investigating the variation of the detected natural frequency, it is shown that the preload level can be diagnosed by the proposed preload feature. Both the experiment results and mathematical model show a direct correlation between the natural frequency and preload levels. Natural frequency increases when the preload level increases. This study provides a method to monitor the preload of a ball screw system which can be used as an indicator of the health status of the drive system.
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Magnetically levitated (Maglev) vehicles maintain a constant air gap between guideway and car bogie, and thereby achieves non-contact riding. Since the straightness and the flatness of the guideway directly affect the stability of levitation as well as the ride comfort, it is necessary to monitor the status of the guideway and to alert the train operators to any abnormal conditions. In order to develop a signal processing algorithm that extracts guideway irregularities from sensor data, virtual testing using a simulation model would be convenient for analyzing the exact effects of any input as long as the model describes the actual system accurately. Simulation model can also be used as an estimation model. In this paper, we develop a state-space dynamic model of a maglev vehicle system, running on the guideway that contains jumps. This model contains not only the dynamics of the vehicle, but also the descriptions of the power amplifier, the anti-aliasing filter and the sampling delay. A test rig is built for the validation of the model. The test rig consists of a small-scale maglev vehicle, tracks with artificial jumps, and various sensors measuring displacements, accelerations, and coil currents. The experimental data matches well with those from the simulation model, indicating the validity of the model.
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