A new, to the best of our knowledge, method based on the linear active disturbance rejection control (LADRC) algorithm is proposed to design and build a vertical vibration isolator below
0.1
H
z
for cold-atom interferometry. This system combines the control system that is used to process and to reduce feedback vibrations measured by a seismometer and a voice coil that is used to cancel the motion of a commercial passive vibration isolation platform. When the feedback is on, the vertical vibration is reduced by an additional factor of up to 1000 from 0.1 to
5
H
z
, while the system has a steady oscillation with a natural period of 66 s. The experimental results show that the LADRC is an effective control algorithm for this application and performs better than the classic lag compensation filters in our case. In addition, there are fewer parameters to adjust in this method, and we only need to tune the feedback gain of the system state error for its application.
An ultra-low frequency vibrational noise isolation apparatus from external vibration can be a critical factor in many fields such as precision measurement, high-technology manufacturing, scientific instruments, and gravitational wave detection. To increase the accuracies of these experiments, well performed vibration isolation technology is required. Until recently the cold atom gravimeter has played a crucial role in measuring the acceleration due to gravity and earth gravity gradient. The vibration isolation is one of the key techniques in the cold atom gravimeter. To reduce the vibrational noise caused by the reflecting mirror of Raman beams in the cold atom gravimeter, a compact active low-frequency vibration isolation system based on sliding-mode robust control is designed and demonstrated. The sliding-mode robust control active vibration isolation method is used to solve the vibration problem of Raman mirror in the cold atomic gravimeter. The purpose of vibration control is that the controller enables the system to be at zero state as the system states are away from the equilibrium due to vibration disturbance. In this system, the mechanical setup is based on a commercial passive isolation platform which only plays a role at higher frequency. A sliding-mode robust control subsystem is used to process and feed back the vibration measured by a seismometer which can measure the velocity of the ground vibration. A voice coil actuator is used to control and cancel the motion of a passive vibration isolation platform. The simulation and experiment results of vibration isolation platform show, on the one hand, that the vibration noise power spectral density decreases by up to 99.9%, and that the phase noise in cold atom interferometry produced by vibration decreases by up to nearly 85.3% compared with the results of the passive vibration isolation platform. On the other hand, compared with the lead-lag control method, the vibration noise power spectral density decreases by up to 83.3% and the phase noise in cold atom interferometry produced by vibration decreases by nearly 40.2%. Therefore, the sliding-mode robust control has the advantages of less tuning parameters, strong anti-interference ability, and more obvious vibration isolating effect.
Active vibration isolation is gaining increased attention in the ultra-high precision application of atom interferometry to effectively treat the unavoidable ground vibration. In this system, a digital control subsystem is used to process and feedback the vibration measured by a seismometer. A voice coil actuator is used to control and cancel the motion of a commercial passive vibration isolation platform. The system level simulation model is established by Simulink software, The simulation results demonstrate the asymptotic stability of the system and the robustness of the control algorithm. Compared with the conventional lead-lag compensation type controller, the algorithm adopted uses sliding mode control, taking advantage of its easy computer implementation and its robust high performance properties. With the feedback path closed, the system acts like a spring system with a natural resonance frequency of 0.02 Hz. The vibration noise in the vertical direction is about 20 times reduced during 0.1 and 2 Hz, The experimental results verify that the isolator has significant vibration isolation performance, and it is very suitable for applications in high precision gravity measurement.
Ultrasonic guided wave is widely used to detect cracks in rail because of its long propagation distance and small attenuation. To effectively detect the fatigue crack in rail bottom through ultrasonic guided wave, an improved principal component analysis-support vector machine (PCA-SVM) intelligent algorithm based on grid search (GS) is proposed to detect the fatigue crack at different depths of rail bottom. The finite element method is used to establish the model of ultrasonic guided wave at different depths of the rail bottom, and the simulation model is compared with the experimental data to determine the effectiveness of the simulation model. Five main component features of the fatigue cracks at different depths are extracted by PCA. The GS method is used to optimize the penalty factor c and kernel function parameter g in the SVM, and the optimized SVM model is selected to identify the rail fatigue crack at different depths. The combination of theoretical simulation and experimental results shows that the accuracy of the training set and the test set of the improved PCA-SVM intelligent algorithm based on the GS method can reach 99.79 % and 99.73 %, respectively, which provides a basis and method for the detection of the fatigue crack depth of the rail bottom.
SVM algorithm in load forecasting needs artificial experience to set the parameters of C and kernel parameters of , which will have a certain impact on the adaptability of the model. Therefore, the SVM has some shortcomings in the selection of model parameters: when meeting the large sample data modeling, parameter adjustment range will increase. Meanwhile, the number of model adjustments is too much and the modeling efficiency will be reduced. In this paper, we use the adaptive ability of group genetic algorithm to optimize the parameters in SVM so as to improve the optimization of the model.
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