Controller Design for Multivariable Linear Time-Invariant Unknown Systems

This paper deals with the design of multivariable controllers for stable linear time-invariant multi-input multi-output systems, with an unknown mathematical model, subject to constant reference/disturbance signals. We propose a new controller parameter optimization approach, which can be carried ou...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 58; no. 9; pp. 2292 - 2306
Main Authors Wang, William S. W., Davison, Daniel E., Davison, Edward J.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Actuators
Closed loop systems
Large-scale systems
multivariable control
optimal control
Optimization
parameter optimization
Performance analysis
Process control
proportional-integral-derivative (PID) control
robust servomechanism problem
servocompensator
Steady-state
Tuning
tuning controllers
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Summary:This paper deals with the design of multivariable controllers for stable linear time-invariant multi-input multi-output systems, with an unknown mathematical model, subject to constant reference/disturbance signals. We propose a new controller parameter optimization approach, which can be carried out experimentally without knowledge of the plant model or the order of the system. The approach has the advantages that controllers can be tuned by perturbing only the initial conditions of the servocompensator, and that the order of the resulting controller can be specified by the designer. Implementation of the proposed controller design approach is described, and an experimental application study of the proposed method applied to a multivariable system with industrial sensor/actuator components is presented to illustrate the feasibility of the design method in an industrial environment.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2013.2258812