A design algorithm using external perturbation to improve Iterative Feedback Tuning convergence

• Iterative Feedback Tuning constitutes an attractive control loop tuning method for processes in the absence of process insight. It is a purely data driven approach for optimization of the loop performance. The standard formulation ensures an unbiased estimate of the loop performance cost function gradient, which is used in a search algorithm for minimizing the performance cost. A slow rate of convergence of the tuning method is often experienced when tuning for disturbance rejection. This is due to a poor signal to noise ratio in the process data. A method is proposed for increasing the data information content by introducing an optimal perturbation signal in the tuning algorithm. The theoretical analysis is supported by a simulation example where the proposed method is compared to an existing method for acceleration of the convergence by use of optimal prefilters.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Keyword

Controller tuning

Direct tuning

Iterative Feedback Tuning

Iterative schemes

Control loop

Data informations

Data-driven approach

External perturbations

Loop performance

Optimal perturbation

Performance costs

Prefilters

Process data

Rate of convergence

Search Algorithms

Simulation example

Tuning algorithm

Tuning method

Unbiased estimates

Algorithms

Approximation theory

Cost benefit analysis

Disturbance rejection

Optimization

Signal to noise ratio

Time varying networks

Convergence of numerical methods

SRA - ICT

SRA - Informations- och kommunikationsteknik

Publication and Content Type

ref (subject category)

art (subject category)