Ters sarkaçta yükseltme yöntemleri incelemesi

Abstract

Ters sarkaç sistemleri, doğrusal olmayan, kararsız ve eksik tahrikli doğası nedeniyle kontrol yöntemlerinin kıyaslanması ve doğrulanmasında temel bir kıstas olarak kabul edilmektedir. Bu çalışmada, ters sarkacın aşağı sarkık konumdan dikey konuma getirilmesini sağlayan yükseltme (swing-up) yöntemleri literatür ışığında kapsamlı bir şekilde incelenmiştir. Lineer ve dönen ters sarkaç sistemleri matematik modelleriyle birlikte sunulmuş, literatürde yer alan ters sarkaç yükseltme yöntemleri yapılan çalışmalarla birlikte verilmiş, ters sarkacın dengede tutulması için kullanılan yöntemler listelenmiş ve literatürde yer alan ters sarkaç yükseltme yöntemleri yapılan çalışmalarla birlikte verilmiştir. Kullanılan yöntemler ilk ortaya konduğu çalışmalardan başlayıp güncel uygulamalarıyla da değerlendirilmiştir. Bu bağlamda, klasik enerji tabanlı yöntemlerin yumuşak robotik (soft robotics) uygulamalarındaki yeni rolleri ele alınmış; bulanık mantık tabanlı sistemlerde hesaplama yükünü optimize eden Hedge Cebiri (Hedge Algebras) yaklaşımı incelenmiştir. Ayrıca, modelden bağımsız kontrol imkânı sunan Derin Pekiştirmeli Öğrenme (Deep Reinforcement Learning) algoritmalarının, özellikle "Simülasyondan Gerçeğe Transfer" (Sim-to-Real) zorluklarını aşmadaki başarısı güncel yayınlar üzerinden tartışılmıştır. Bu derleme çalışması, geleneksel kontrol yaklaşımlarından yapay zekâ odaklı öğrenen sistemlere kadar geniş bir perspektif sunarak, araştırmacılar için güncel ve bütünsel bir yol haritası niteliği taşımaktadır.

Keywords

• Doğrusal ters sarkaç
• döner ters sarkaç
• sarkaç yükseltme
• klasik kontrol
• pekiştirmeli öğrenme

Supporting Institution

Balıkesir Üniversitesi Bilimsel Araştırma Projeleri Birimi

Project Number

2018/082

A review on swing-up methods in inverted pendulums

Abstract

Inverted pendulum systems are considered a fundamental criterion in comparing and validating control methods due to their nonlinear, unstable, and underactuated nature. In this study, swing-up methods that raise the inverted pendulum from a hanging position to a vertical position are comprehensively examined in light of the literature. Linear and rotary inverted pendulum systems are presented along with their mathematical models, and inverted pendulum swing-up methods found in the literature are presented along with the studies conducted,, the methods used to keep the inverted pendulum in equilibrium are listed, and inverted pendulum swing-up methods found in the literature are presented along with the studies conducted. The methods used are evaluated starting from their first proposed studies and including their current applications. In this context, the new roles of classical energy-based methods in soft robotics applications are discussed; the Hedge Algebra approach, which optimizes the computational load in fuzzy logic-based systems, is examined. In addition, the success of Deep Reinforcement Learning algorithms, which offer model-independent control, especially in overcoming "Sim-to-Real" challenges, is discussed through current publications. This review provides a broad perspective, ranging from traditional control approaches to AI-driven learning systems, offering a current and comprehensive roadmap for researchers.

Keywords

• Linear inverted pendulum
• rotary inverted pendulum
• pendulum swing-up
• classical control
• reinforcement learning

Supporting Institution

Balıkesir University Scientifis Research Projects Unit

Project Number

2018/082

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