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Towards an Optimal Learning for Robust Iterative-Based Intelligent PID

[+] Author Affiliations
Elmira Madadi, Dirk Söffker

University of Duisburg-Essen, Duisburg, Germany

Paper No. DSCC2016-9712, pp. V001T02A003; 6 pages
doi:10.1115/DSCC2016-9712
From:
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5069-5
  • Copyright © 2016 by ASME

abstract

This contribution considers a model-free control method based on an optimal iterative learning control framework to design a suitable controller. Using this framework, the controller requires neither the information about the systems dynamical structure nor the knowledge about system physical behaviors. The task is solved using only the system outputs and inputs, which are assumed as measurable. The structure of the proposed method consists of three parts. The first part is implemented through an intelligent PID controller on the system. In the second part, a robust second order differentiator via sliding mode is applied in order to estimate accurately the evolution of the state function. In the third part, an optimal iterative learning control is chosen to improve the performance. Numerical examples are shown to demonstrate the successful application and performance of the method.

Copyright © 2016 by ASME

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