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Trajectory tracking of wheeled mobile robot by adopting iterative learning control with predictive, current, and past learning items

Published online by Cambridge University Press:  01 April 2014

Chong Yu  and
Xiong Chen
Chong Yu*
Affiliation:
School of Information Science and Technology, Fudan University, Shanghai 200433, China
Xiong Chen*
Affiliation:
School of Information Science and Technology, Fudan University, Shanghai 200433, China
*
*Corresponding author. E-mail: [email protected], [email protected]
E-mail: [email protected]
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Summary

In this paper, an iterative learning control algorithm is adopted to solve the high-precision trajectory tracking issue of a wheeled mobile robot with time-varying, nonlinear, and strong-coupling dynamics properties. The designed iterative learning control law adopts predictive, current and past learning items to drive the state variables, and input variables, and outputs variables converge to the bounded scope of their desired values. The algorithm can enhance the control performance, stability and robust characteristics. The rigorous mathematical proof of the convergence character of the proposed iterative learning control algorithm is given. The feasibility, effectiveness, and robustness of the proposed algorithm are illustrated by quantitative experiments and comparative analysis. The experimental results show that the proposed iterative learning control algorithm has an outstanding control effect on the trajectory tracking issue of wheeled mobile robots.

Keywords

Wheeled mobile robotIterative learning controlTrajectory trackingPredictive learning itemsCurrent learning itemsPast learning itemsProof of the convergence character
Type
Articles
Information
Robotica , Volume 33 , Issue 7 , August 2015 , pp. 1393 - 1414
Copyright
Copyright © Cambridge University Press 2014 

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