TY - JOUR

T1 - Design and application of PIP controllers: robust control of the IFAC93 benchmark

AU - Taylor, C. James

AU - Chotai, Arun

AU - Young, Peter C.

PY - 2001/8/1

Y1 - 2001/8/1

N2 - Proportional-integral-plus (PIP) controllers exploit the full power of optimal state variable feedback within a nonminimum state space (NMSS) setting. They are simple to implement and provide a logical extension of conventional proportional-integral/proportional-integral-derivative (PI/PID) controllers, with additional dynamic feedback and input compensators introduced automatically when the process is of greater than first order or has appreciable pure time delays. The present paper provides a tutorial introduction to the NMSS/PIP control design methodology and associated system identification procedure. The latter is based on the utilization of the simplified refined instrumental variable (SRIV) algorithm for the estimation of transfer function models. The practical utility of these techniques is illustrated by their application to the IFAC93 benchmark system, a seventh-order stochastic simulation whose parameters vary randomly within specified ranges. This benchmark provides a good simulation example for tutorial purposes, since it requires the control engineer to work through all the usual design steps, including identification of a low-order control model, control system design, and implementation using a standard programming language, in this case ‘C’. Finally, note that the statistical estimation tools described in the paper have been assembled as a tool-box within the MatlabTM software environment.

AB - Proportional-integral-plus (PIP) controllers exploit the full power of optimal state variable feedback within a nonminimum state space (NMSS) setting. They are simple to implement and provide a logical extension of conventional proportional-integral/proportional-integral-derivative (PI/PID) controllers, with additional dynamic feedback and input compensators introduced automatically when the process is of greater than first order or has appreciable pure time delays. The present paper provides a tutorial introduction to the NMSS/PIP control design methodology and associated system identification procedure. The latter is based on the utilization of the simplified refined instrumental variable (SRIV) algorithm for the estimation of transfer function models. The practical utility of these techniques is illustrated by their application to the IFAC93 benchmark system, a seventh-order stochastic simulation whose parameters vary randomly within specified ranges. This benchmark provides a good simulation example for tutorial purposes, since it requires the control engineer to work through all the usual design steps, including identification of a low-order control model, control system design, and implementation using a standard programming language, in this case ‘C’. Finally, note that the statistical estimation tools described in the paper have been assembled as a tool-box within the MatlabTM software environment.

KW - Non-minimal state space

KW - proportional-integral plus

KW - multi-objective optimisation

KW - IFAC93 benchmark

U2 - 10.1177/014233120102300304

DO - 10.1177/014233120102300304

M3 - Journal article

VL - 23

SP - 183

EP - 200

JO - Transactions of the Institute of Measurement and Control

JF - Transactions of the Institute of Measurement and Control

SN - 1477-0369

IS - 3

ER -