Adaptive fuzzy tracking for flexible-joint robots with random noises via command filter control

2021 ◽  
Author(s):  
Wei Sun ◽  
Shuzhen Diao ◽  
Shun-Feng Su ◽  
Yuqiang Wu
Keyword(s):  
Flexible Joint ◽  
Adaptive Fuzzy ◽  
Flexible Joint Robots ◽  
Command Filter

scholarly journals Adaptive fuzzy practical tracking control for flexible-joint robots via command filter design

2020 ◽  
Vol 53 (5-6) ◽  
pp. 814-823
Author(s):  
Shuzhen Diao ◽  
Wei Sun ◽  
Wenxing Yuan
Keyword(s):  
Fuzzy System ◽  
Tracking Control ◽  
Filter Design ◽  
Limited Time ◽  
Flexible Joint ◽  
First Order ◽  
Flexible Joint Robots ◽  
Complexity Problem ◽  
Command Filter ◽  
Compensation Signal

This paper investigates the issue of finite-time tracking control for flexible-joint robots. In the design scheme, the unknown continuous function is identified by a fuzzy system. By introducing the command filter technique, “explosion of complexity” problem which arises from repeated differentiation of virtual controllers is avoided. Meanwhile, errors resulting from the first-order filters can be reduced with the introduced compensation signal. Besides, the proposed method ensures that the tracking performance could be achieved within a limited time. Eventually, the simulation is given to demonstrate the effectiveness of the proposed scheme.

scholarly journals Extended and Unscented Kalman Filtering Applied to a Flexible-Joint Robot with Jerk Estimation

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Mohammad Ali Badamchizadeh ◽  
Iraj Hassanzadeh ◽  
Mehdi Abedinpour Fallah
Keyword(s):  
Initial Conditions ◽  
Covariance Matrices ◽  
Flexible Joints ◽  
Velocity Measurements ◽  
Lagrange Equations ◽  
Robust Nonlinear Control ◽  
Flexible Joint ◽  
Flexible Joint Robots ◽  
Simulation Results

Robust nonlinear control of flexible-joint robots requires that the link position, velocity, acceleration, and jerk be available. In this paper, we derive the dynamic model of a nonlinear flexible-joint robot based on the governing Euler-Lagrange equations and propose extended and unscented Kalman filters to estimate the link acceleration and jerk from position and velocity measurements. Both observers are designed for the same model and run with the same covariance matrices under the same initial conditions. A five-bar linkage robot with revolute flexible joints is considered as a case study. Simulation results verify the effectiveness of the proposed filters.

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