No abstract

A hybrid (i.e., physics-guided data-driven) feedforward tracking controller is proposed for systems with unmodeled linear or nonlinear dynamics. The proposed controller is based on the filtered basis functions (FBF) approach, and hence called a hybrid FBF controller. It formulates the feedforward control input to a system as a linear combination of a set of basis functions whose coefficients are selected to minimize tracking errors. To predict the system response and thereby the tracking errors, the basis functions are filtered using a combination of two linear models. The first model is physics-based and remains unaltered during the execution of the controller, while the second is data-driven and is continuously updated during the execution of the controller. To ensure its practicality and safe learning, the proposed hybrid FBF controller is equipped with the abilities to handle delays in data acquisition and to detect impending instability due to its inherent data-driven feedback loop. The effectiveness of the hybrid FBF controller is demonstrated via application to vibration compensation of a 3D printer with unmodeled linear and nonlinear dynamics. Thanks to the proposed hybrid FBF controller, the tracking accuracy of the 3D printer and the print quality are both significantly improved in experiments involving high-speed printing, compared to standard FBF controller that does not incorporate a data-driven model. Furthermore, the ability of the hybrid FBF controller to detect, and hence to potentially avoid, impending instability is demonstrated offline using data collected online from experiments.

Objective Previous studies have shown an increased in psychiatric disorders in women with disorders associated with hyperandrogenism, but few nationwide cohorts have studied this phenomenon. Therefore, this study is aimed to examine the association between the clinical manifestations of hyperandrogenism and subsequent psychiatric disorders. Methods Based on the National Health Insurance Research Database, 49,770 enrolled participants were matched for age and index date between January 1, 2000, and December 31, 2015. Hirsutism, polycystic ovary syndrome, and acne are characterized by hyperandrogenism. After adjusting for confounding factors, we used Cox proportional analysis to compare the risk of psychiatric disorders during the 16 years of follow-up. Results Of all the participants, 1319 (13.25%) had psychiatric disorders in the study group, whereas only 3900(9.80%) had psychiatric disorders in the control group. After adjusting for age, and monthly income, the Cox regression analysis showed that the study patients were more likely to develop psychiatric disorders (hazard ratio [HR]: 2.004, 95% confidence interval [CI] = 1.327–2.724, P < 0.001). The results demonstrated that women aged 20–29 years had a more significant risk. Conclusion Women with clinical characteristics of hyperandrogenism have a higher risk of developing psychiatric disorders, especially those aged 20–29 years.

A hybrid (i.e., physics-guided data-driven) feedforward tracking controller is proposed for systems with unmodeled linear or nonlinear dynamics. The controller is based on the filtered basis function (FBF) approach, hence it is called a hybrid FBF controller. It formulates the feedforward control input to a system as a linear combination of a set of basis functions whose coefficients are selected to minimize tracking errors. The basis functions are filtered using a combination of two linear models to predict and minimize the tracking errors. The first model is physics-based and remains unaltered during the execution of the controller, while the second is data-driven and is continuously updated during the execution of the controller. To ensure its practicality and safe learning, the proposed hybrid FBF controller is equipped with the ability to handle delays in data acquisition and to detect impending instability due to its inherent data-driven feedback loop. Its effectiveness is demonstrated via application to vibration compensation of a 3D printer with unmodeled linear and nonlinear dynamics. Thanks to the proposed hybrid FBF controller, the tracking accuracy of the 3D printer is significantly improved in experiments involving high-speed printing, compared to a standard FBF controller that does not incorporate a data-driven model. Furthermore, the ability of the hybrid FBF controller to detect and, hence, potentially avoid impending instability is demonstrated offline using data collected online from experiments.