State Space Constrained Iterative Learning Control for Robotic Manipulators

2017 ◽  
Vol 20 (3) ◽  
pp. 1145-1150 ◽  
Author(s):  
Kaloyan Yovchev ◽  
Kamen Delchev ◽  
Evgeniy Krastev
Keyword(s):  
State Space ◽  
Iterative Learning Control ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Iterative Learning

Input and Output Constraints in Iterative Learning Control Design for Robotic Manipulators

2018 ◽  
Vol 06 (03) ◽  
pp. 197-208 ◽  
Author(s):  
Gijo Sebastian ◽  
Ying Tan ◽  
Denny Oetomo ◽  
Iven Mareels
Keyword(s):  
Iterative Learning Control ◽  
Internal State ◽  
Tracking Error ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Feed Forward Control ◽  
Output Constraints ◽  
Rehabilitation Robotic

Motivated by the safety requirement of rehabilitation robotic systems for after stroke patients, this paper handles position or output constraints in robotic manipulators when the patients repeat the same task with the robot. In order to handle output constraints, if all state information is available, a state feedback controller can ensure that the output constraints are satisfied while iterative learning control (ILC) is used to learn the desired control input through iterations. By incorporating the feedback control using barrier Lyapunov function with feed-forward control (ILC) carefully, the convergence of the tracking error, the boundedness of the internal state, the boundedness of input signals can be guaranteed along with the satisfaction of the output constraints over iterations. The effectiveness of the proposed controller is demonstrated using simulations from the model of EMU, a rehabilitation robotic system.

scholarly journals Finding the Optimal Parameters for Robotic Manipulator Applications of the Bounded Error Algorithm for Iterative Learning Control

2017 ◽  
Vol 47 (4) ◽  
pp. 3-11 ◽  
Author(s):  
Kaloyan Yovchev
Keyword(s):  
Computer Simulation ◽  
Iterative Learning Control ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Robotic Manipulator ◽  
Iterative Learning ◽  
Optimal Parameters ◽  
Manipulator Arm ◽  
Bounded Error ◽  
Optimal Values

Abstract This paper continues previous research of the Bounded Error Algorithm (BEA) for Iterative Learning Control (ILC) and its application into the control of robotic manipulators. It focuses on investigation of the influence of the parameters of BEA over the convergence rate of the ILC process. This is performed first through a computer simulation. This simulation suggests optimal values for the parameters. Afterwards, the estimated results are validated on a physical robotic manipulator arm. Also, this is one of the first reports of applying BEA into robots control.

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

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