scholarly journals T-S Fuzzy H∞ Tracking Control of Input Delayed Robotic Manipulators

10.5772/15982 ◽  
2011 ◽  
Author(s):  
Haiping Du ◽  
Weihua Li
Keyword(s):  
Tracking Control ◽  
Robotic Manipulators

scholarly journals Adaptive Neural Tracking Control of Robotic Manipulators with Guaranteed NN Weight Convergence

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Yang ◽  
Jing Na ◽  
Guanbin Gao ◽  
Chao Zhang
Keyword(s):  
Adaptive Control ◽  
Tracking Control ◽  
Estimation Error ◽  
Tracking Error ◽  
Robotic Manipulators ◽  
Adaptive Laws ◽  
Filter Operation ◽  
Control Response ◽  
Unknown Nonlinear Dynamics ◽  
Acceleration Signals

Although adaptive control for robotic manipulators has been widely studied, most of them require the acceleration signals of the joints, which are usually difficult to measure directly. Although neural networks (NNs) have been used to approximate the unknown nonlinear dynamics in the robotic systems, the conventional adaptive laws for updating the NN weights cannot guarantee that the obtained NN weights converge to their ideal values, which could degrade the tracking control response. To address these two issues, a new adaptive algorithm with the extracted NN weights error is incorporated into adaptive control, where a novel leakage term is superimposed on the gradient method. By using the Lyapunov approach, the convergence of both the tracking error and the estimation error can be guaranteed simultaneously. In addition, two auxiliary functions are introduced to reformulate the robotic model for designing the adaptive law, and a filter operation is used to avoid measuring the acceleration signals. Comparisons to other well-recognized adaptive laws are given, and extensive simulations based on a 2-DOF SCARA robotic system are given to verify the effectiveness of the proposed control strategy.

A composite adaptive output feedback tracking controller for robotic manipulators

Robotica ◽  
1999 ◽  
Vol 17 (6) ◽  
pp. 591-600 ◽  
Author(s):  
E. Zergeroglu ◽  
W. Dixon ◽  
D. Haste ◽  
D. Dawson
Keyword(s):  
Output Feedback ◽  
Tracking Control ◽  
Prediction Error ◽  
Tracking Error ◽  
Robotic Manipulators ◽  
Parameter Estimate ◽  
Position Tracking ◽  
Linear Filter ◽  
Velocity Measurements ◽  
Feedback Tracking

This paper provides a solution to the composite adaptive output feedback tracking control problem for robotic manipulators. The proposed controller utilizes an update law that is a composite of a gradient update law driven by the link position tracking error and a least squares update law driven by the prediction error. In order to remove the controller's dependence on link velocity measurements, a linear filter and a new prediction error formulation are designed. The controller provides semi-global asymptotic link position tracking performance. Experimental results illustrate that the proposed controller provides improved link position tracking error transient performance and faster parameter estimate convergence in comparsion to the same controller using a gradient update law.

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