Aktif Araç Süspansiyon Sistemi İçin Makine Öğrenimi Tabanlı Kontrol Sisteminin Geliştirilmesi

Abstract

Bu çalışmada, araç aktif süspansiyon sistemini (VASS) kontrol etmek için makine öğrenme yöntemlerinde biri olan Gaussian süreci (GP) algoritması tasarlanmıştır. Deneysel veriler denetimli öğrenme yöntemi (regresyon yöntemi) ile eğitildi. Veriler, tam durum geri beslemeli optimal kontrol yaklaşımına dayalı olarak ayarlanmış optimal bir doğrusal ikinci dereceden kontrolörden elde edildi. Sonuçlar, önerilen makine öğrenme (ML) tabanlı yere nüfuz eden radar (GPR) denetleyicisinin, yaylı kütle konumundaki salınımı azaltma açısından, sırasıyla kare ve rastgele yol koşulları için sırasıyla %15 ve %21,64 azalma ile optimal denetleyiciden daha iyi performans ortaya koyduğunu göstermiştir.

Keywords

• Aktif Araç Süspansiyonu
• Makine Öğrenme Yöntemleri
• Kontrol
• Doğrusal Kuadratik Optimal Kontrol
• Rastgele Yol Profili

Development of Machine Learning Based Control System for Vehicle Active Suspension System

Abstract

In this paper, Gaussian process (GP) algorithm, which is one of the machine learning methods, is designed to control the vehicle active suspension system (VASS). Experimental data were trained by supervised learning method (regression method). The data were obtained from an optimal linear quadratic controller tuned based on a full state feedback optimal control approach. The results demonstrated that the proposed machine learning (ML) based ground-penetrating radar (GPR) controller outperforms the optimal controller under uncertainties in terms of reducing the oscillation in sprung mass position with a 15% and 21.64% reduction for square and random road conditions, respectively.

Keywords

• Vehicle Active Suspension system
• Machine Learning Methods
• Control
• Linear Quadratic Optimal Control
• Random Road Profile.

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