Transactions on Control, Automation and Systems Engineering

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Model Reference Adaptive Control Using Non-Euclidean Gradient Descent

  • Lee, Sang-Heon (Research Center for Advanced manufacturing research, University of South Austrialia) ;
  • Robert Mahony (Dept. Systems Engineering, Research school of Information Science and Engineering, Australian National University) ;
  • Kim, Il-Soo (Dept. of Mechanical Engineering, Mokpo National University)
  • Published : 2002.12.01
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Abstract

In this Paper. a non-linear approach to a design of model reference adaptive control is presented. The approach is demonstrated by a case study of a simple single-pole and no zero, linear, discrete-time plant. The essence of the idea is to generate a full non-linear model of the plant dynamics and the parameter adaptation dynamics as a gradient descent algorithm with respect to a Riemannian metric. It is shown how a Riemannian metric can be chosen so that the modelled plant dynamics do in fact match the true plant dynamics. The performance of the proposed scheme is compared to a traditional model reference adaptive control scheme using the classical sensitivity derivatives (Euclidean gradients) for the descent algorithm.

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