Tracking control of piezoceramic actuators by using preisach model

2005 ◽  
Author(s):  
Xiaofeng Zhou ◽  
Shixi Yang ◽  
Guoning Qi ◽  
Xiaoping Hu
Keyword(s):  
Tracking Control ◽  
Preisach Model

Shear stress tracking control of an electrorheological fluid considering hysteresis non-linearity

2004 ◽  
Vol 218 (2) ◽  
pp. 183-194 ◽  
Author(s):  
Y M Han ◽  
S B Choi
Keyword(s):  
Shear Stress ◽  
Tracking Control ◽  
Silicone Oil ◽  
Electrorheological Fluid ◽  
Preisach Model ◽  
Temperature Dependent ◽  
Model Inversion ◽  
Control Robustness ◽  
Discrete Manner ◽  
Er Fluid

This paper presents shear stress tracking control of an electrorheological (ER) fluid actuator subjected to the hysteresis non-linearity. As a first step, polymethylaniline (PMA) particles are prepared and mixed with silicone oil to make an ER fluid. The Couette-type electroviscometer is employed to achieve the field-dependent shear stress. The Preisach model for the PMA-based ER fluid is identified using experimental first-order descending (FOD) curves. A compensation strategy is then formulated in a discrete manner through the Preisach model inversion to achieve the desired shear stress of the ER fluid. A proportional-intergal-derivative (PID) feedback controller is also integrated with the compensator in order to guarantee control robustness to uncertainty due to temperature-dependent hysteresis variation. The tracking performance of the control strategy is experimentally evaluated for two different desired shear stress trajectories.

Development of hysteresis friction model for a precise tracking control system

2011 ◽  
Vol 226 (5) ◽  
pp. 1338-1344 ◽  
Author(s):  
J Choi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Tracking Control ◽  
Sliding Mode ◽  
Back Propagation ◽  
Friction Model ◽  
Ball Screw ◽  
Preisach Model ◽  
The Neural Network ◽  
Hysteresis Friction

Hysteresis friction model for a tracking control system of the mechanical system was proposed in this article. Hysteresis phenomena within a microscopic displacement range were verified through experiments, and Preisach model was applied to the proposed friction model. To implement the Preisach model, the neural network algorithm was used to replace the distribute function of Preisach model. A weighing vector of the neural network was updated through the back-propagation learning method. The performance of the proposed hysteresis friction model was evaluated through the tracking control of a ball-screw system. In order to design the tracking control system, sliding mode controller was designed and the proposed friction model was applied as a feed-forward term of controller.

Nonlinear Tracking Control of Spacecraft with MPC: LPV Approach

2016 ◽  
Vol 15 (0) ◽  
pp. 133-140
Author(s):  
Atsushi SAKAMOTO ◽  
Yuichi IKEDA ◽  
Isao YAMAGUCHI ◽  
Takashi KIDA
Keyword(s):  
Tracking Control ◽  
Nonlinear Tracking
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