Probability distribution modelling to improve stability in nonlinear MIMO control

Herzallah, Randa and Lowe, David Probability distribution modelling to improve stability in nonlinear MIMO control. IN: Proceedings of 2003 IEEE Conference on Control Applications (CCA). IEEE.

Abstract

We consider the direct adaptive inverse control of nonlinear multivariable systems with different delays between every input-output pair. In direct adaptive inverse control, the inverse mapping is learned from examples of input-output pairs. This makes the obtained controller sub optimal, since the network may have to learn the response of the plant over a larger operational range than necessary. Moreover, in certain applications, the control problem can be redundant, implying that the inverse problem is ill posed. In this paper we propose a new algorithm which allows estimating and exploiting uncertainty in nonlinear multivariable control systems. This approach allows us to model strongly non-Gaussian distribution of control signals as well as processes with hysteresis. The proposed algorithm circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider.

Publication DOI: https://doi.org/10.1109/CCA.2003.1223139
Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Non-linearity and complexity research group
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Additional Information: ©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Event Title: 2003 IEEE Conference on Control Applications
Event Type: Other
Event Dates: 2003-06-23 - 2003-06-25
Uncontrolled Keywords: statistical distributions,multivariable control systems,stability,MIMO systems,nonlinear control systems,hysteresis,dynamic programming,neural nets,probability distribution modelling,nonlinear MIMO control,direct adaptive inverse con,Control and Systems Engineering

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