The control of anti-lock braking system is a great challenge, because of the nonlinear and complex characteristics of braking dynamics, unknown parameters of vehicle environment and sy ...

A new class of the almost orthogonal filters is described in this article. These filters are a generalization of the classical orthogonal filters commonly used in the circuit theory, control system theory, signal processing, and process identification. Almost orthogonal filters generate the series of almost orthogonal Legendre functions over the interval (0, ∞). It is well known that all real systems suffer from some imperfections, so the models of these systems should reflect this fact. A new method for obtaining an imperfect system model is proposed. This method uses an almost orthogonal filter, which is based on almost orthogonal functions. Experiments with modular servo drive were performed to validate theoretical results and demonstrate that the method described in the article is suitable for modelling of imperfect systems.

Object recognition and classification as well as obstacle distance calculation are of the utmost importance in today’s autonomous driving systems. One such system designed to detect obstacle and track intrusion in railways is considered in this paper. The heart of this system is the decision support system (DSS), which is in charge of making complex decisions, important for a safe and efficient autonomous train drive based on the information obtained from various sensors. DSS determines the object class and its distance from a running train by analyzing sensor images using machine learning algorithms. For the quality training of these machine learning models, it is necessary to provide training sets with images of adequate quality, which is often not the case in real-world railway applications. Furthermore, the images of insufficient quality should not be processed at all in order to save computational time. One of the most common types of distortion which occurs in real-world conditions (train movement and vibrations, movement of other objects, bad weather conditions, and day and night image differences) is blur. This paper presents an improved edge-detection method for the automatic detection and rejection of images of inadequate quality regarding the blur level. The proposed method, with its improvements convenient for railway application, is compared with several other state-of-the-art methods for blur detection, and its superior overall performance is demonstrated.