Adaptive Iterative Learning Control Algorithm with Large Uncertainties in System Parameters
In this paper, an adaptive iterative learning control (AILC) algorithm has implemented by using the least squares approximation. A new method for calculating the learning gain of ILC algorithm has implemented. The ILC algorithm has been applied for a SISO linear time-invariant (LTI) dynamic system with unknown parameters, and a parameter identification algorithm is designed to optimize the accurate values of the unknown parameters and minimize the tracking error. A simulation study is used for testing the implemented method. Simulations show that AILC algorithm is suitable for linear systems that have unknown parameters, but the bounds of these parameters are limited. The controller is robust when the parameters have known bounds.
Keywords: Iterative Learning Control, Parameter Identification, Unknown parameters, Repetitive system, Learning gain, Adaptive control system.
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ABOUT THE AUTHOR
Habib Ghanbarpour Asl
received his B.S., M.S., and Ph.D. in aerospace engineering, flight dynamic, and control division, from the Amirkabir and Sharif Universities of Technology in 1996, 1999 and 2007, respectively. He is an Assistant Professor in the Department of Mechatronics Engineering at the University of Turkish Aeronautical Association. His research interests include Guidance, Control, Navigation, Robotics, Sensors, and Flying Robots.
Habib Ghanbarpour Asl
received his B.S., M.S., and Ph.D. in aerospace engineering, flight dynamic, and control division, from the Amirkabir and Sharif Universities of Technology in 1996, 1999 and 2007, respectively. He is an Assistant Professor in the Department of Mechatronics Engineering at the University of Turkish Aeronautical Association. His research interests include Guidance, Control, Navigation, Robotics, Sensors, and Flying Robots.
