1 / 16

Evaluation of model-based predictive control - PowerPoint PPT Presentation

Evaluation of model-based predictive control. Student: Daniel Czarkowski Supervisor: Tom O’Mahony date 25/03/2003. Overview. Background Model Based – Predictive Control Generalised Predictive control Models Benchmarks: GPC versus PI. MBPC. Features of MBPC

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

PowerPoint Slideshow about ' Evaluation of model-based predictive control' - farrah

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Evaluation of model-based predictive control

Student: Daniel Czarkowski

Supervisor: Tom O’Mahony

date 25/03/2003

• Background Model Based – Predictive Control

• Generalised Predictive control

• Models

• Benchmarks: GPC versus PI

• Features of MBPC

• All of them use a process model

• The optimum control sequence is obtained through the minimization of a cost index

• Only the first element of this sequence is transmitted to the plant as the current control u(t) (receding horizon)

• Model Based Predictive Control can be achieved according to:

• The type of model used

• The type of cost function used

• The optimization method applied

• CARIMA model

• Cost function

• Implementation of a Genetic Algorithm for minimization IAE:

• Servo response

• Regulatory disturbance

• Combined

• The models of benchmarked plant were taken from Astrom

ZN: 6.25

Lambda: 13.79

Non-Convex:5.07

PI controller

• GPC

n1=1 n2=2 nu=1 λ=1*10-6

T-polynomial=(1-0.63*z-1)

Sampling Period = 0.7 (sec.)

IAE=0.91

• PI controller

k=0.862 ki=0.461

IAE=5.07

• Ts=0.7sec. IAE=0.81

• Ts=0.1sec. IAE=0.3

n1=2 n2=3 nu=1 λ=1*10-6T-polynomial=1+0.9*z-1

• Fourth Order System:

• GPC

• n1=2 n2=3 nu=1 λ=1*10-6 Tpoly=1+0.293*z-1

• IAE=0.23

• PI controller

• k=2.74 ki=4.08

• IAE=0.82

Nonminimum-phase model

GPC

n1=4 n2=4 nu=1 Ts=0.83

Tpoly=(1-0.224*z-1)3

IAE=8.10

PI controller

k=0.294 ki=0.184

IAE=14,4

• The Åström benchmark test was developed for PI controller

• A Genetic Algorithm was implemented for tuning GPC controller

• Part of comparison has been done