Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a nov...Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a novel method via T-S cloud inference network optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constructed with T-S fuzzy neural network and the cloud model. So, the rapid of fuzzy logic and the uncertainty of cloud model for processing data are both taken into account. What's more, GA possesses good parallel design structure and global optimization characteristics. Compared with the simulation recognition results of traditional BP Algorithm, GA is more accurate and effective. Moreover, virtual reality technology is introduced into the field of shape control by Lab VIEW, MATLAB mixed programming. And virtual flatness pattern recognition interface is designed.Therefore, the data of engineering analysis and the actual model are combined with each other, and the shape defects could be seen more lively and intuitively.展开更多
The application of new-designed levitation controller requires extensive validation prior to enter into commercial service. However, huge mounts of approximations and assumptions lead the theoretical analysis away fro...The application of new-designed levitation controller requires extensive validation prior to enter into commercial service. However, huge mounts of approximations and assumptions lead the theoretical analysis away from the engineering practice. The experimental methods are time-consuming and financial expensive, even unrealizable due to the lack of suitable sensors. Numerical simulations can bridge the gap between the theoretical analysis and experimental techniques. A complete overall dynamic model of maglev levitation system is derived in this work, which includes the simple-supported bridges, the calculation of electromagnetic force with more details, the stress of levitation modules and the cabin. Based on the aforementioned model, it shows that the inherent nonlinearity, inner coupling, misalignments between the sensors and actuators, and self-excited vibration are the main issues that should be considered during the design process of controller. Then, the backstepping controller based on the mathematical model of the module with reasonable simplifications is proposed, and the stability proofs are listed. To show the advantage of controller, two numerical simulation experiments are carried out. Finally, the results illustrating closed-loop performance are provided.展开更多
基金Project(LJRC013)supported by the University Innovation Team of Hebei Province Leading Talent Cultivation,China
文摘Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a novel method via T-S cloud inference network optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constructed with T-S fuzzy neural network and the cloud model. So, the rapid of fuzzy logic and the uncertainty of cloud model for processing data are both taken into account. What's more, GA possesses good parallel design structure and global optimization characteristics. Compared with the simulation recognition results of traditional BP Algorithm, GA is more accurate and effective. Moreover, virtual reality technology is introduced into the field of shape control by Lab VIEW, MATLAB mixed programming. And virtual flatness pattern recognition interface is designed.Therefore, the data of engineering analysis and the actual model are combined with each other, and the shape defects could be seen more lively and intuitively.
基金Projects(60404003,11202230)supported by the National Natural Science Foundation of China
文摘The application of new-designed levitation controller requires extensive validation prior to enter into commercial service. However, huge mounts of approximations and assumptions lead the theoretical analysis away from the engineering practice. The experimental methods are time-consuming and financial expensive, even unrealizable due to the lack of suitable sensors. Numerical simulations can bridge the gap between the theoretical analysis and experimental techniques. A complete overall dynamic model of maglev levitation system is derived in this work, which includes the simple-supported bridges, the calculation of electromagnetic force with more details, the stress of levitation modules and the cabin. Based on the aforementioned model, it shows that the inherent nonlinearity, inner coupling, misalignments between the sensors and actuators, and self-excited vibration are the main issues that should be considered during the design process of controller. Then, the backstepping controller based on the mathematical model of the module with reasonable simplifications is proposed, and the stability proofs are listed. To show the advantage of controller, two numerical simulation experiments are carried out. Finally, the results illustrating closed-loop performance are provided.
