Adaptive Backstepping Control for Constrained Systems Using Nonlinear Mapping

2014 ◽  
Vol 39 (9) ◽  
pp. 1558-1563 ◽  
Author(s):  
Tao GUO ◽  
Ding-Lei WANG ◽  
Ai-Min WANG
2021 ◽  
pp. 107754632199887
Author(s):  
Sinan Basaran ◽  
Fevzi Cakmak Bolat ◽  
Selim Sivrioglu

Many structural systems, such as wind turbines, are exposed to high levels of stress during operation. This is mainly because of the flow-induced vibrations caused by the wind load encountered in every tall structure. Preventing the flow-induced vibration has been an important research area. In this study, an active electromagnetic mass damper system was used to eliminate the vibrations. The position of the stabilizer mass in the active electromagnetic mass damper system was determined according to the displacement information read on the system without using any spring element, unlike any conventional system. The proposed system in this study has a structure that can be implemented as a vibration suppressor in many intelligent structural systems. Two opposing electromagnets were used to determine the instant displacement of the stabilizer mass. The control currents to be given to these electromagnets are determined by using an adaptive backstepping control design. The adaptive controller algorithm can predict the wind load used in the controller design without prior knowledge of the actual wind load. It was observed that the designed active electromagnetic mass damper structure is successful in suppressing system vibrations. As a result, the proposed active electromagnetic mass damper system has been shown to be suitable for structural systems in flow-induced vibration damping.


2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Le Liang ◽  
Yanjie Liu ◽  
Hao Xu

Multiobjective trajectory optimization and adaptive backstepping control method based on recursive fuzzy wavelet neural network (RFWNN) are proposed to solve the problem of dynamic modeling uncertainties and strong external disturbance of the rubber unstacking robot during recycling process. First, according to the rubber viscoelastic properties, the Hunt-Crossley nonlinear model is used to construct the robot dynamics model. Then, combined with the dynamic model and the recycling process characteristics, the multiobjective trajectory optimization of the rubber unstacking robot is carried out for the operational efficiency, the running trajectory smoothness, and the energy consumption. Based on the trajectory optimization results, the adaptive backstepping control method based on RFWNN is adopted. The RFWNN method is applied in the main controller to cope with time-varying uncertainties of the robot dynamic system. Simultaneously, an adaptive robust control law is developed to eliminate inevitable approximation errors and unknown disturbances and relax the requirement for prior knowledge of the controlled system. Finally, the validity of the proposed control strategy is verified by experiment.


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