MIMO U-model based control: real-time tracking control and feedback analysis via small gain theorem

Authors:
Syed Saad Azhar Ali;Muhammad Shafiq;Fouad M. Al-Sunni;Jamil M. Bakhashwain
Affiliations:
Air University, Electrical Engineering Department, Islamabad, Pakistan;Ghulam Ishaq Khan Institute, Department of Electronic Engineering, Topi, Swabi, Pakistan;King Fahd University of Petroleum & Minerals, Systems Engineering Department, Dhahran, Saudi Arabia;King Fahd University of Petroleum & Minerals, Electrical Engineering Department, Dhahran, Saudi Arabia
Venue:
WSEAS Transactions on Circuits and Systems
Year:
2008

Citing 7
Cited 0

Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Numerical Mathematics and Computing

Numerical Mathematics and Computing
Performance analysis of three step positive input shaping

ACMOS'08 Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation
Learning feedforward control of MIMO nonlinear systems using U-model

CA '07 Proceedings of the Ninth IASTED International Conference on Control and Applications
A time-domain feedback analysis of filtered-error adaptive gradientalgorithms

IEEE Transactions on Signal Processing
H∞ optimality of the LMS algorithm

IEEE Transactions on Signal Processing
Supervised learning of perceptron and output feedback dynamic networks: a feedback analysis via the small gain theorem

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, MIMO U-model based IMC is used for the tracking control of multivariable nonlinear systems. The algorithm is implemented in real-time on a 2DoF robot arm. The stability and convergence issues for the control-oriented U-model are also discussed. In order to guarantee stability and faster convergence speeds, bounds are suggested for the learning rate of adaptation algorithm that estimate the parameters of U-model. The adaptation algorithm is first associated with a feedback structure and then its stability is investigated using l2 stability and small gain theorem. The paper also discusses about the robustness of adaptation algorithm in the presence of noise and suggests optimal choices for faster convergence speeds.