MODELLING OF UNKNOWN MARKOV JUMP SYSTEMS AND PID CONTROL TUNED BY ADOLESCENT IDENTITY SEARCH ALGORITHM

Öz

Markov jump systems (MJS) are difficult to control due to unknown dynamics, random transitions and environmental noises. In this paper, real-time linear MJSs are reviewed to improve general modeling and control performance using meta-heuristic optimization methods. Contributions are twofold as: i) black-box identification using collected data from a real-time RLC circuit, ii) first use of the Adolescent Identity Search algorithm (AISA), which is a meta-heuristic optimization method in the design of a proportional-integral-derivative (PID) controller. For this purpose, an Extreme Learning Machine (ELM) model is constructed to model and predict the dynamics of unknown MJs. Then, the optimal set of PID parameters are found using the ELM model in batch optimization. To optimize the parameters of the controller, meta-heuristic algorithms commonly used in the literature are compared with AISA. According to the simulation results, the parameters of the real-time PID controller have been estimated with an error rate of 0.005 with AISA, which achieved the best fittness value in the shortest time. The proposed approach is applied to model and control an experimental RLC circuit with Markovian behavior.

Anahtar Kelimeler

• Markov Jump Systems
• Extreme Machine Learning
• Pid Control
• Meta-Heuristic Optimization
• AISA

BİLİNMEYEN MARKOV ATLAMALI SİSTEMLERİN MODELLEMESİ VE ERGEN KİMLİK ARAMA ALGORİTMASI İLE AYARLANMIŞ PID KONTROLÜ

Öz

Markov atlama sistemlerinin (Markov Jump System–MJS), bilinmeyen dinamikler, rastgele geçişler ve çevresel gürültüler nedeniyle denetlenmesi zordur. Bu makalede, gerçek zamanlı doğrusal MJS'ler optimizasyon yöntemleri kullanılarak genel modelleme ve denetim performansını iyileştirmek için gözden geçirilmiştir. Bu çalışmayla elde edilen katkılar iki başlıkta değerlendirilmektedir: i) gerçek zamanlı bir RLC devresinden toplanan veriler kullanılarak kara-kutu tanımlama, ii) oransal-integral-türev (Proportional-Integral-Derivative - PID) denetleyicinin tasarımında sezgisel optimizasyon yöntemi olan Ergen Kimliği Arama algoritmasının (AISA) ilk kez kullanımı. Bu amaçla, bilinmeyen MJ'lerin dinamiklerini modellemek ve tahmin etmek için bir Aşırı Öğrenme Makinesi (Extreme Learning Machine- ELM) modeli oluşturulmuştur. Ardından, yığın optimizasyon içerisinde ELM modeli kullanılarak en uygun PID parametreleri kümesi bulunmuştur. Denetleyicinin parametrelerini optimize etmek için literatürde yaygın olarak kullanılan meta-sezgisel algoritmalar AISA ile karşılaştırılmıştır. Simülasyon sonuçlarına göre en iyi uygunluk değerine en kısa sürede ulaşan AISA ile gerçek zamanlı PID denetleyicisine ait parametreler 0.005 hata oranı ile tahmin edilmiştir. Önerilen yaklaşım, Markov davranışı sergileyen deneysel bir RLC devresinin modellenmesi ve denetimi için uygulanmıştır.

Anahtar Kelimeler

• Markov Atlamalı Sistemler
• Aşırı Öğrenme Makineleri
• PID Denetim
• Meta-Sezgisel Optimizasyon
• AISA

Kaynakça

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