The fourth industrial revolution led to advanced servo systems, enhancing productivity across industries. However, designing these systems remains challenging due to the performance-stability trade-off. This paper presents a model-based motion control of a linear motor motion stage in frequency domain. A user-code for the PowerPMAC commercial controller was developed to excite motion control system so that we could get a frequency response. The theoretical frequency response of the servo algorithm was compared with the experimental frequency response. Based on this, a tuning graphical user interface (GUI) was developed to predict performance when the servo loop gain is changed. Especially, to compensate for residual vibrations caused by high acceleration and deceleration and to improve tracking error, DOB (Disturbance Observer) and ILC (Iterative Learning Control) control techniques were applied in the frequency domain. Through the design of the frequency domain motion controller, the control performance of the linear motor motion stage could be predicted with over 96% accuracy, resulting in a 54.32% improvement in tracking error and a 93.56% improvement in settling time, 85.29% in RMS error.
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Fuzzy Neural Network Control for a Reaction Force Compensation Linear Motor Motion Stage Kyung Ho Yang, Hyeong-Joon Ahn International Journal of Precision Engineering and Manufacturing-Smart Technology.2024; 2(2): 109. CrossRef
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Drone is an innovative industry that can combine the application of various technologies in the fourth industrial era, such as big data, artificial intelligence, and ICT. Although the synergy effects of these technologies will be great in various industrial ecosystems, drones are vulnerable to gusts such as "building wind" or "valley wind". Herein, the frequency domain of a mini drone was identified and a model-based disturbance observer (DOBs) was applied to implement the drone robust resistance against gusts. The frequency response of the Parrot Mambo or mini drone was measured with multi-sine excitation and the system dynamic parameters were identified. Based on the identified model, DOBs were designed and applied to the drone’s altitude, position, and yaw control. The effectiveness of the DOBs was verified with a sinusoidal disturbance. With the model-based DOB, 84.5% of the drone altitude responses, 50.7% of x responses, 52.1% of y responses, and 79.7% of yaw responses against sinusoidal disturbances were reduced. Flight responses were measured against wind disturbances with changing speed and direction. With the model-based DOBs, the drone"s altitude decreased by 87.7%, the x position by 53.0%, the y position by 60.6%, and the yaw angle by 56.2%.
If fatigue failure occurs during aircraft operation, it can cause catastrophic injuries. So, it is necessary to study fatigue failure at the design stage. Frequency domain fatigue analysis is used to predict fatigue failure. During frequency domain fatigue analysis, results can be calibrated by PSD analysis. In this study, fatigue failure is predicted by the Dirlik method, Lalanne method and Steinberg method. Regarding results, life determined by the Dirlik method, Lalanne method and Steinberg method were 8.737, 8.314, and 7.901 times the standard life, respectively. The Steinberg method is the most conservative but the difference with other methods was approximately 10%. In the cycle histogram, the Dirlik method had more counts than the Lalanne method in lower stress range. However, it does not affect the life of material used in this study. However, if material has a lower fatigue limit or stronger PSD data is used, life difference will occur.
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