# Steering Dynamic and Hybrid Steering Control of a Novel Micro-Autonomous Railway Inspection Car
## 1 Introduction  
Rail transportation plays a substantial role in the economic development of many countries. Metro, subway, and high-speed trains are environmentally friendly transportation systems that are often used to enable long-distance, local, or smart city mobility [1]. The speed and number of trains on high-speed railways have increased in modern transit systems, and consequently, track defect inspection has become increasingly essential for ensuring passenger safety [1, 2]. Rail transportation organizations regularly use a variety of manual inspection procedures, among which visual inspections and testing with gauges are the most common. Manual inspection devices are straightforward and reliable but have slow measuring speeds [3]. Additionally, manual inspection enables the assessment of only small, short-track railway sections and does not allow for full track measuring or ongoing track monitoring. These shortcomings have led to greater investment in the study, development, and application of self-propelled railway inspection vehicles [4]. Modern inspection technologies utilize advanced sensors, including ultrasonic sensors, LiDAR, and cameras, to detect defects like surface degradation, irregularities, and fastener failures with high precision [5]. Current rail surface testing equipment has been introducedin the form of the Felix track inspection robot, the JDT track inspection robot, and the self-propelled inspection kart RailPod [4]. However, researchers studying these smart inspection vehicles face challenges in managing lateral dynamics and maintaining the operational stability of the device during operation under various damaged track conditions. In recent years, research on active steering control methods for railway vehicles has received much attention from researchers who want to enhance the stability and performance of autonomous railway vehicles. Recent research on active steering control for railway vehicles has focused on optimizing stability, reducing wear, and improving operational efficiency. [PERSON] et al. (2020) proposed a minimum wear number-based control strategy, enhancing wheel-rail interaction and extending service life [6]. Later, [PERSON] et al. (2023) explored advanced control structures to improve vehicle maneuverability [7]. [PERSON] et al. (2020) investigated wheelset steering from a wear evolution perspective, while [PERSON] et al. (2021) assessed active steering performance using simulations and roller rig tests [8; 9]. These studies highlight the growing emphasis on intelligent steering solutions, forming the foundation for further advancements in hybrid and autonomous railway systems. Although these methods provide improved feedback and tracking accuracy, they often require significant energy input and complex computations. Moreover, most active steering systems ignore the natural self-guiding forces of the wheelset, such as slip force and normal force, which can contribute to stability with lower energy requirements. One of the fundamental challenges in railway vehicle dynamics is ensuring lateral stability while minimizing computational complexity. Traditional vehicle motion equations rely on simplified linear approximations that often fail to capture the nonlinear interactions between the wheelset and track, leading to higher positioning errors and stability issues. To address the challenges of maintaining stability in autonomous railway inspection vehicles, this study proposes an advanced hybrid steering control system, which integrates the self-steering capability of the wheelset with an active steering controller based on fuzzy logic. This system is specifically designed for a micro-autonomous railway inspection vehicle, optimizing lateral stability and minimizing deviation from the track centerline. The conical wheelset inherently enables self-steering on curved tracks.