Research on Robust Tracking Control Based Improved Fuzzy Disturbance Observer for Flight Simulator

2011 ◽  
Vol 148-149 ◽  
pp. 1170-1174
Author(s):  
Zheng Hua Liu ◽  
Xiang Yu ◽  
Yue Yang Hua

Fuzzy disturbance observer, as an outstanding feedback controller, is an effective means to improve the accuracy and bandwidth of high precision angular tracking systems. This robust controller based on the fuzzy disturbance observer(FDO) is introduced in this paper, and the method is applied to the flight simulator system together with PD controller. Compared to the traditional method of lead and lag correction or DOB controller, this novel method based on FDO can inhibit high-frequency noise and compensate low-frequency interference better, such as frictions, and achieve better precision finally.

2011 ◽  
Vol 328-330 ◽  
pp. 2215-2219
Author(s):  
Xiang Yu ◽  
Zheng Hua Liu ◽  
Yan Ren

The compound axis control technique is an effective means to improve the accuracy and bandwidth of high precision electro-optical tracking systems. However, traditional methods of lead and lag correction can not achieve ideal performance. The robust control method based on the disturbance observer (DOB) is introduced in this paper, and the method is applied to the electro-optical tracking system together with the Kalman Filter. Compared to the traditional method of lead and lag correction, the method based on DOB can inhibit high-frequency noise and compensate for low-frequency interference better, such as frictions, and achieve better precision finally.


1999 ◽  
Vol 121 (2) ◽  
pp. 261-269 ◽  
Author(s):  
Bong Soo Kang ◽  
Soo Hyun Kim ◽  
Yoon Keun Kwak ◽  
Craig C. Smith

This paper presents a robust controller for tracking control of a direct-drive robot. The proposed controller consists of two portions: a computed torque method which precompensates for dynamics of the modeled plant and an H∞ controller which postcompensates for residual errors which are not completely removed by the computed torque method. Experimental methods for identifying appropriate model structure and parameters are presented, and three specific controller types are compared. Using the robot designed in our laboratory, the combined controller reduced tracking errors by one half compared to computed torque control alone, and by one sixth compared to conventional independent joint control.


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