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PID tuning with Ant Colony Optimization (ACO): A framework for a step response based tuning algorithm
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
2018 (English)Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The building automation industry lacks an affordable, simple, solution for autonomous PID controller tuning when overhead variables fluctuate. In this project, requested by Jitea AB, a solution was developed, utilising step response process modelling, numerical integration of first order differential equations, and Ant Colony Optimization (ACO). The solution was applied to two control schemes; simulated outlet flow from a virtual water tank, and the physical air pressure in the ventilation system of a preschool in Sweden. An open-loop step response provided the transfer function in each case, which, after some manipulation, could be employed to predict the performance of any given set of PID parameters, based on a weighted cost function. This prediction model was used in ACO to find optimal settings. The program was constructed in both Structured Control Language and Structured Text and documented in an approachable way. The results showed that the program was, in both cases, able to eliminate overshoot and retain the settling time (with a slightly raised rise time) achieved with settings tuned per the current methods of Jitea AB. Noise and oscillations present in the physical system did not appear to have any major negative influence on the tuning process. The program performed above Jitea AB’s expectation, and will be tested in more scenarios, as it showed promise. Autonomous implementation could be of societal benefit through increased efficiency and sustainability in a range of processes. In future studies, focus should be on improving the prediction model, and further optimising the ACO variables.

Abstract [sv]

Byggnadsautomationsbranschen saknar en kostnadseffektiv lösning för att autonomt trimma in PID-regulatorer när överordnade variabler fluktuerar. I detta (av Jitea AB beställda) arbete, utvecklades en lösning baserad på stegsvarsmodellering, numerisk integration av första gradens ordinära differentialekvationer och myrkolonisoptimering (ACO). Lösningen applicerades i två regleringsfall; en simulerad utloppsventil från en virtuell vattentank, och det fysiska lufttrycket i ventilationssystemet på en förskola i Sverige. Ett stegsvar med öppen slinga gav en överföringsfunktion i respektive fall, som efter viss manipulering kunde nyttjas för att förutspå prestandan för en uppsättning PID-parametrar baserat på en samlad, viktad kostnadsfunktion. Predikteringsmodellen implementerades i ACO för att finna optimala parametrar. Programmet konstruerades i Structured Control Language och Structured Text, och dokumenterades på ett pedagogiskt sätt. Resultaten visade att programmet (i båda fallen) klarade att eliminera översläng med bibehållen stabiliseringstid (och något förskjuten stigningstid) jämfört med Jitea AB:s existerande trimningsmetod. Signalbrus och oscillationer i det fysiska systemet verkade inte ha någon avsevärd negativ inverkan på trimningsprocessen. Programmet presterade över Jitea AB:s förväntan, och kommer (med tanke på de lovande resultaten) fortsatt att testas i fler scenarion. Implementation av en autonom version skulle kunna innebära flera samhälleliga förmåner i form av ökad verkningsgrad och hållbarhet i en rad processer. I framtida studier bör fokus läggas på att ytterligare förbättra prediktionsmodellen, samt att vidare utforska de optimala myrkolonisvariablerna.

Place, publisher, year, edition, pages
2018. , p. 79
Keywords [en]
PID, self-tuning, control system, building automation, ant colony optimization, neural networks, open-loop, closed-loop, step response.
Keywords [sv]
PID, självtrimning, reglersystem, byggnadsautomation, myrkolonioptimering, neurala nätverk, öppen slinga, sluten slinga, stegsvar.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-33903Local ID: ET-V18-G3-007OAI: oai:DiVA.org:miun-33903DiVA, id: diva2:1223474
Subject / course
Electrical Engineering ET2
Educational program
Automationsingenjör TAUMG 180 GR
Supervisors
Examiners
Available from: 2018-06-25 Created: 2018-06-25 Last updated: 2018-06-25Bibliographically approved

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