Robustness Characteristics of Model-Based Iterative Learning Controllers for Robotic Manipulators

2005 ◽  
Author(s):  
B. Porter ◽  
S.S. Mohamed
Keyword(s):  
Robotic Manipulators ◽  
Iterative Learning ◽  
Model Based

scholarly journals Iterative learning control with sampled-data feedback for robot manipulators

2014 ◽  
Vol 24 (3) ◽  
pp. 299-319 ◽  
Author(s):  
Kamen Delchev ◽  
George Boiadjiev ◽  
Haruhisa Kawasaki ◽  
Tetsuya Mouri
Keyword(s):  
Feedback Control ◽  
Iterative Learning Control ◽  
Feedforward Control ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Sampled Data ◽  
Model Based ◽  
Data Feedback

Abstract This paper deals with the improvement of the stability of sampled-data (SD) feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC) with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more), while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached

scholarly journals Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators

2019 ◽  
Vol 35 (1) ◽  
pp. 124-134 ◽  
Author(s):  
Thomas George Thuruthel ◽  
Egidio Falotico ◽  
Federico Renda ◽  
Cecilia Laschi
Keyword(s):  
Reinforcement Learning ◽  
Closed Loop ◽  
Dynamic Control ◽  
Robotic Manipulators ◽  
Model Based

A Gaussian process mixture model-based hard-cut iterative learning algorithm for air quality prediction

2019 ◽  
Vol 85 ◽  
pp. 105789 ◽  
Author(s):  
Yatong Zhou ◽  
Xiangyu Zhao ◽  
Kuo-Ping Lin ◽  
Ching-Hsin Wang ◽  
Lingling Li
Keyword(s):  
Air Quality ◽  
Gaussian Process ◽  
Mixture Model ◽  
Learning Algorithm ◽  
Iterative Learning ◽  
Quality Prediction ◽  
Model Based ◽  
Air Quality Prediction
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