Probability distribution modelling to improve stability in nonlinear MIMO control

Randa Herzallah, David Lowe

    Research output: Chapter in Book/Published conference outputConference publication


    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.
    Original languageEnglish
    Title of host publicationProceedings of 2003 IEEE Conference on Control Applications (CCA)
    Number of pages6
    ISBN (Print)0-7803-772-9
    Publication statusPublished - 25 Jun 2003
    Event2003 IEEE Conference on Control Applications - Istanbul, Turkey
    Duration: 23 Jun 200325 Jun 2003


    Conference2003 IEEE Conference on Control Applications
    Abbreviated titleCCA 2003

    Bibliographical note

    ©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.


    • 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


    Dive into the research topics of 'Probability distribution modelling to improve stability in nonlinear MIMO control'. Together they form a unique fingerprint.

    Cite this