Euler Lagrange sistemlerinin uyarlamalı sinir ağları tabanlı çıkış geri beslemeli denetiminde konum sinyalinin sınırlandırılması

Year 2024, Volume: 13 Issue: 1, 116 - 122, 15.01.2024

Bayram Melih Yılmaz

https://doi.org/10.28948/ngumuh.1326508

Abstract

Bu çalışmada, dinamik belirsizliklere sahip Euler Lagrange (EL) sistemlerinin konum takip problemine odaklanılmıştır. Özellikle, konum bilgisi elde edilirken hız bilgisinin kullanılamadığı ya da kullanılamadığı durum incelenmiştir. İkincil bir amaç olarak, her girişin takip hatasının önceden sınırlandırılması hedeflenmiştir. Konum takip hatasının önceden belirlenen bir bölgede olacak şekilde başlatılıp bu bölgede kalacağı kısıtları altında hedeflenen konumun takip edilmesi motivasyonu ile bir denetleyici tasarlanmıştır. EL sistemlerinin modelindeki belirsizliklerin bir kısmı gürbüz bileşenlerle bastırılarak telafi edilmiş, bir kısmı ise yenilikçi güncelleme kuralları kullanılarak yapay sinir ağları yaklaşımıyla kestirilip denetim yapısında kullanılmıştır. Hız ölçümlerinin mevcut olmaması durumunu telafi etmek için filtre temelli yöntemler kullanılmıştır. Konum sinyalinin sınırlı olması koşulu altında uyarlamalı sinir ağları tabanlı çıkış geri beslemeli yenilikçi bir denetleyici tasarlanmış, kapalı çevrim denetim sisteminin kararlılığı Lyapunov tabanlı metotlarla analiz edilmiştir. Tasarlanan denetleyicinin performansının değerlendirilmesi için iki serbestlik derecesine sahip düzlemsel robot modeli kullanılarak karşılaştırmalı benzetim çalışmaları gerçekleştirilmiştir.

Keywords

Çıkış geri beslemeli kontrol, Yapay sinir ağları, Lyapunov yöntemleri

Limitation of position signal in output feedback control based on adaptive neural networks of Euler Lagrange systems

Abstract

In this study, the position tracking problem of Euler-Lagrange (EL) systems with dynamic uncertainties is addressed. Specifically, the case where velocity information is not utilized while obtaining position information is investigated. As a secondary objective, the aim is to pre-bound the tracking error of each input. A controller has been designed with the motivation of tracking the desired position within a predetermined region, initiated with the position tracking error being confined to this region. In the model of EL systems, some of the uncertainties have been suppressed and compensated using robust components, while the remaining uncertainties have been estimated using an artificial neural network approach with innovative update rules and incorporated into the control structure. An innovative controller with output feedback based on adaptive neural networks was designed under the condition of limited position signal, and the stability of the closed-loop control system was analyzed by Lyapunov-based methods. Comparative simulation studies have been conducted to evaluate the performance of the designed controller using a two-degree-of-freedom planar robot model.

Keywords

Output feedback control, Artificial neural network, Lyapunov methods

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