CERES :: Browsing by Author "Zolotas, Argyrios"

Browsing by Author "Zolotas, Argyrios"

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Open Access
A complementary fusion-based multimodal non-destructive testing and evaluation using phased-array ultrasonic and pulsed thermography on a composite structure
(MDPI , 2024-07-11) Torbali, Muhammet E; Zolotas, Argyrios; Avdelidis, Nicolas P; Alhammad, Muflih; Ibarra-Castanedo, Clemente; Maldague, Xavier P
Combinative methodologies have the potential to address the drawbacks of unimodal non-destructive testing and evaluation (NDT & E) when inspecting multilayer structures. The aim of this study is to investigate the integration of information gathered via phased-array ultrasonic testing (PAUT) and pulsed thermography (PT), addressing the challenges posed by surface-level anomalies in PAUT and the limited deep penetration in PT. A center-of-mass-based registration method was proposed to align shapeless inspection results in consecutive insertions. Subsequently, the aligned inspection images were merged using complementary techniques, including maximum, weighted-averaging, depth-driven combination (DDC), and wavelet decomposition. The results indicated that although individual inspections may have lower mean absolute error (MAE) ratings than fused images, the use of complementary fusion improved defect identification in the total number of detections across numerous layers of the structure. Detection errors are analyzed, and a tendency to overestimate defect sizes is revealed with individual inspection methods. This study concludes that complementary fusion provides a more comprehensive understanding of overall defect detection throughout the thickness, highlighting the importance of leveraging multiple modalities for improved inspection outcomes in structural analysis.
Open Access
Attack-detection architectural framework based on anomalous patterns of system performance and resource utilization - Part II
(IEEE, 2021-06-11) Aloseel, Abdulmohsan; Al-Rubaye, Saba; Zolotas, Argyrios; Shaw, Carl
This paper presents a unique security approach for detecting cyber-attacks against embedded systems (ESs). The proposed approach has been shaped within an architectural framework called anomalous resource consumption detection (ARCD). The approach’s detection mechanism detects cyber-attacks by distinguishing anomalous performance and resource consumption patterns from a pre-determinable reference model. The defense mechanism of this approach acts as an additional layer of protection for ESs. This technique’s effectiveness was previously evaluated statistically, and in this paper, we tested this approach’s efficiency computationally by using the support-vector machine algorithm. The datasets were generated and collected based on a testbed model, where it was run repeatedly under different operation conditions (normal cases (Rs) versus attacked cases). The executed attack scenarios are 1) denial-of-service (DoS); 2) brute force (BF); and 3) remote code execution (RCE), and man-in-the-middle (MITM). A septenary tuple model, which consists of seven determinants that are analyzed based on seven statistical criteria, is the core of the detection mechanism. The prediction accuracy in terms of classifying anomalous patterns compared to normal patterns based on the confusion matrix revealed promising results, proving this approach’s effectiveness, where the final results confirmed very high prediction accuracies in terms of distinguishing anomalous patterns from the typical patterns. Integrating the ARCD concept into an operating system’s functionality could help software developers augment the existing security countermeasures of ESs. Adopting the ARCD approach will pave the way for software engineers to build more secure operating systems in line with the embedded system’s capabilities, without depleting its resources.
Open Access
Automated impact damage detection technique for composites based on thermographic image processing and machine learning classification
(MDPI, 2022-11-22) Alhammad, Muflih; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Torbali, Muhammet E.; Genest, Marc; Zhang, Hai; Zolotas, Argyrios; Maldgue, Xavier P. V.
Composite materials are one of the primary structural components in most current transportation applications, such as the aerospace industry. Composite material diagnostics is a promising area in the fight against structural damage in aircraft and spaceships. Detection and diagnostic technologies often provide analysts with a valuable and rapid mechanism to monitor the health and safety of composite materials. Although many attempts have been made to develop damage detection techniques and make operations more efficient, there is still a need to develop/improve existing methods. Pulsed thermography (PT) technology was used in this study to obtain healthy and defective data sets from custom-designed composite samples having similar dimensions but different thicknesses (1.6 and 3.8). Ten carbon fibre-reinforced plastic (CFRP) panels were tested. The samples were subjected to impact damage of various energy levels, ranging from 4 to 12 J. Two different methods have been applied to detect and classify the damage to the composite structures. The first applied method is the statistical analysis, where seven different statistical criteria have been calculated. The final results have proved the possibility of detecting the damaged area in most cases. However, for a more accurate detection technique, a machine learning method was applied to thermal images; specifically, the Cube Support Vector Machine (SVM) algorithm was selected. The prediction accuracy of the proposed classification models was calculated within a confusion matrix based on the dataset patterns representing the healthy and defective areas. The classification results ranged from 78.7% to 93.5%, and these promising results are paving the way to develop an automated model to efficiently evaluate the damage to composite materials based on the non-distractive testing (NDT) technique.
Open Access
Combined active suspension and structural damping control for suppression of flexible bodied railway vehicle vibration
(Taylor & Francis, 2019-02-04) Zheng, Xiang; Zolotas, Argyrios; Goodall, Roger
The design trend for future high speed trains is envisaged to be lightweight, rising the cost of structural vibration due to the extra exibility. In this context, studies have looked into suppression of such vibrations via use of either (conventional actuators) active suspensions or by structural damping via piezoelectric actuators. The addition of extra macro-actuators will highly impact vehicle weight and is subject to location constraints, while the use of only piezo-actuators normally does not reach the required force levels for appropriate suppression. However, piezo-actuators provide appropriate complementary action with conventional active suspension. In this paper, we present a decentralized control scheme for suppressing the vertical vibration of the vehicle body, combining active structural damping via frequency- weighted H2 control and active suspension control using skyhook damping via structured H1 synthesis. A vertical side-view model of a exible-bodied railway vehicle is used for the control study, with the con_guration of piezoelectric actuators and sensors determined via structural norms. Stability robustness of the controller is analysed with respect to parametric and dynamic uncertainties using _ analysis techniques. Results illustrate the e_ectiveness of the proposed control scheme for both exible and rigid modes while guaranteeing robustness to model uncertainty
Open Access
A comprehensive survey of unmanned ground vehicle terrain traversability for unstructured environments and sensor technology insights
(Elsevier, 2023-09-29) Beyçimen, Semih; Ignatyev, Dmitry; Zolotas, Argyrios
This article provides a detailed analysis of the assessment of unmanned ground vehicle terrain traversability. The analysis is categorized into terrain classification, terrain mapping, and cost-based traversability, with subcategories of appearance-based, geometry-based, and mixed-based methods. The article also explores the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) and other based end-to-end methods as crucial components for advanced terrain traversability analysis. The investigation indicates that a mixed approach, incorporating both exteroceptive and proprioceptive sensors, is more effective, optimized, and reliable for traversability analysis. Additionally, the article discusses the vehicle platforms and sensor technologies used in traversability analysis, making it a valuable resource for researchers in the field. Overall, this paper contributes significantly to the current understanding of traversability analysis in unstructured environments and provides insights for future sensor-based research on advanced traversability analysis.
Open Access
A comprehensive survey on Delaunay Triangulation: applications, algorithms, and implementations over CPUs, GPUs, and FPGAs
(IEEE, 2024-01-15) Elshakhs, Yahia S.; Deliparaschos, Kyriakos M.; Charalambous, Themistoklis; Oliva, Gabriele; Zolotas, Argyrios
Delaunay triangulation is an effective way to build a triangulation of a cloud of points, i.e., a partitioning of the points into simplices (triangles in 2D, tetrahedra in 3D, and so on), such that no two simplices overlap and every point in the set is a vertex of at least one simplex. Such a triangulation has been shown to have several interesting properties in terms of the structure of the simplices it constructs (e.g., maximising the minimum angle of the triangles in the bi-dimensional case) and has several critical applications in the contexts of computer graphics, computational geometry, mobile robotics or indoor localisation, to name a few application domains. This review paper revolves around three main pillars: (I) algorithms, (II) implementations over central processing units (CPUs), graphics processing units (GPUs), and field programmable gate arrays (FPGAs), and (III) applications. Specifically, the paper provides a comprehensive review of the main state-of-the-art algorithmic approaches to compute the Delaunay Triangulation. Subsequently, it delivers a critical review of implementations of Delaunay triangulation over CPUs, GPUs, and FPGAs. Finally, the paper covers a broad and multi-disciplinary range of possible applications of this technique.
Open Access
Cybersecurity of embedded systems a novel approach for detecting cyberattacks based on anomalous patterns of resource utilisation
(Cranfield University, 2022-01) Aloseel, Abdulmohsan; Al-Rubaye, Saba; Zolotas, Argyrios
An embedded system (ES) is a processing unit that has been embedded into a larger cyber-physical system (CPS) to steer its functions. The ES has played an essential role in modern life, where it has been used widely in sensing, controlling and computing for countless applications in different domains, such as the internet of things (IoT), smart cities, healthcare, transportation, communication, military, transportation, gas distribution, avionics and national infrastructures. Due to its widespread application in different domains and its evolution in conjunction with many key technologies, it is crucial that these systems are secured against cyberattacks as the ES has the same generic security goals – confidentiality, integrity and availability – as conventional computer systems. Although the ES is exposed to the numerous and unpredicted security threats that are experienced by conventional computer systems, it is significantly limited in its ability to manage the advanced security solutions that are implemented on conventional computer systems. The limitations in resources of the ES, due to its identity or characteristics, impose tight constraints on both its communication and computing capacity, thereby hindering the implementation of advanced security solutions. Thus, the cybersecurity of an ES is limited by constraints on its resources rather than by the absence of advanced security solutions. There is an urgent need, therefore, to develop security solutions that are compatible with the capabilities of the ES. This study tried to bridge the gap by addressing both theoretical and empirical aspects of ES cybersecurity. The study can be divided into three main blocks. The first block identifies the key factors, involved parties or entities, and creates the cybersecurity landscape for embedded systems (CSES), while considering the conflict between the requirements for cybersecurity and the computing capabilities of an ESs. Additionally, twelve factors influencing CSES have been extracted and identified based on the direction of the research. These factors have been used to shape a multiple layers feedback framework of embedded system cybersecurity (MuLFESC), with nine layers of protection. It has been developed in line with an expanded model of risk assessment metrics, which will enable cybersecurity practitioners to evaluate the security countermeasures of their systems and assist in the development of more comprehensive solutions for CSES. A novel security approach, called anomalous resource consumption detection (ARCD), was developed in the second block of this study. This involved the design of a testbed to provide a realistic hardware-software environment to analyse an example application of an ES. A Smart PiCar was run repeatedly under different operational conditions – typical conditions and under attack. The data of seven designated parameters based on seven statistical criteria was analysed to measure the range, pattern of performance and resource utilisation. The results from this statistical analysis demonstrated the potential for defining a standard pattern for the resource utilisation and performance of the embedded system due to a significant similarity with the values of the parameters at normal states. In contrast, the results from the attacked cases showed a definite and detectable impact on the consumption and performance of the resources of the ES, which presented anomalous patterns. The ARCD method can be implemented as an additional layer of protection to detect cyber-attacks in an ES, where a septenary tuple model, consisting of seven parameters, is the core of the detection mechanism. In the final block, the ARCD approach has been placed within an architectural framework, which may pave the way for software engineers to build secure operating systems in line with the capabilities of the ES. The architectural framework was developed after the efficiency of the approach was computationally validated by machine learning. This involved the design of a classifier and predictor model to find the predictive accuracy percentage in terms of separating patterns of anomalous performance and resource utilisation from the typical pattern. Based on the confusion matrix, the prediction accuracy for classifying anomalous patterns compared with default patterns revealed promising results, thus proving the effectiveness of the ARCD approach. The results confirmed very high prediction accuracies as regards distinguishing anomalous patterns from the typical patterns.
Open Access
Data supporting: 'Development of a thermal excitation source used in an active thermographic UAV platform'
(Cranfield University, 2022-08-31 16:49) Deane, Shakeb; Tsourdos, Antonios; Avdelidis, Nico; Zolotas, Argyrios; P. V. Maldague, Xavier; Ibarra-Castanedo, Clemente; Genest, Marc; Pant, Shashank; Williamson, Alex; Withers, Stephen; Ahmadi, Mohammadali
This work aims to address the effectivenessand challenges of using active infrared thermography (IRT) on-board an unmannedaerial vehicle (UAV) platform. The work seeks to assess the performance ofsmall low powered forms of excitation which are suitable for activethermography and the ability to locate subsurface defects on composites. Anexcitation source in the form of multiple 250 W lamps are mounted onto a UAVand are solely battery powered with a remote trigger to power cycle them.Multiple experiments address the interference from the UAV whilst performing anactive IRT inspection. The optimal distances and time required for a UAV inspection using IRT is calculated. Multiple signal processing techniques areused to analyse the composites which helps locate the sub-surface defects. It was observedthat a UAV can successfully carry the required sensors and equipment for anActive thermographic NDT inspection which can provide access to difficult areas. Most active thermographic inspection equipment is large, heavy, and expensive. Furthermore, using such equipment for inspection of complexstructures is time-consuming. For example, a cherry picker would be required toinspect the tail of an aircraft. This solution looks to assist engineersinspecting complex composite structures and could potentially significantly reduce the time and cost of a routine inspection.
Open Access
Development of a thermal excitation source used in an active thermographic UAV platform
(Taylor & Francis, 2022-06-03) Deane, Shakeb; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Williamson, Alex A.; Withers, Stephen; Zolotas, Argyrios; Maldague, Xavier P. V.; Ahmadi, Mohammad; Pant, Shashank; Genest, Marc; Rabearivelo, Hobivola A.; Tsourdos, Antonios
This work aims to address the effectiveness and challenges of using active infrared thermography (IRT) onboard an unmanned aerial vehicle (UAV) platform. The work seeks to assess the performance of small low-powered forms of excitation which are suitable for active thermography and the ability to locate subsurface defects on composites. An excitation source in multiple 250 W lamps is mounted onto a UAV and is solely battery powered with a remote trigger to power cycle them. Multiple experiments address the interference from the UAV whilst performing an active IRT inspection. The optimal distances and time required for a UAV inspection using IRT are calculated. Multiple signal processing techniques are used to analyse the composites which help locate the sub-surface defects. It was observed that a UAV can successfully carry the required sensors and equipment for an Active thermographic NDT inspection which can provide access to difficult areas. Most active thermographic inspection equipment is large, heavy, and expensive. Furthermore, using such equipment for the inspection of complex structures is time-consuming. For example, a cherry picker would be required to inspect the tail of an aircraft. This solution looks to assist engineers in inspecting complex composite structures and could potentially significantly reduce the time and cost of a routine inspection.
Open Access
Diagnosis of composite materials in aircraft applications: towards a UAV active thermography inspection approach
(Society of Photo-Optical Instrumentation Engineers (SPIE), 2021-04-12) Alhammad, Muflih; Avdelidis, Nicolas Peter; Deane, Shakeb; Ibarra-Castanedo, Clemente; Pant, Shashank; Nooralishahi, Parham; Ahmadi, Mohammad; Genest, Marc; Zolotas, Argyrios; Zanotti Fragonara, Luca; Valdes, Julio J.; Maldague, Xavier P. V.
Diagnosis and prognosis of failures for aircrafts’ integrity are some of the most important regular functionalities in complex and safety-critical aircraft structures. Further, development of failure diagnostic tools such as Non-Destructive Testing (NDT) techniques, in particular, for aircraft composite materials, has been seen as a subject of intensive research over the last decades. The need for diagnostic and prognostic tools for composite materials in aircraft applications rises and draws increasing attention. Yet, there is still an ongoing need for developing new failure diagnostic tools to respond to the rapid industrial development and complex machine design. Such tools will ease the early detection and isolation of developing defects and the prediction of damages propagation; thus allowing for early implementation of preventive maintenance and serve as a countermeasure to the potential of catastrophic failure. This paper provides a brief literature review of recent research on failure diagnosis of composite materials with an emphasis on the use of active thermography techniques in the aerospace industry. Furthermore, as the use of unmanned aerial vehicles (UAVs) for the remote inspection of large and/or difficult access areas has significantly grown in the last few years thanks to their flexibility of flight and to the possibility to carry one or several measuring sensors, the aim to use a UAV active thermography system for the inspection of large composite aeronautical structures in a continuous dynamic mode is proposed.
Open Access
End-to-end one-shot path-planning algorithm for an autonomous vehicle based on a convolutional neural network considering traversability cost
(MDPI, 2022-12-10) Bian, Tongfei; Xing, Yang; Zolotas, Argyrios
Path planning plays an important role in navigation and motion planning for robotics and automated driving applications. Most existing methods use iterative frameworks to calculate and plan the optimal path from the starting point to the endpoint. Iterative planning algorithms can be slow on large maps or long paths. This work introduces an end-to-end path-planning algorithm based on a fully convolutional neural network (FCNN) for grid maps with the concept of the traversability cost, and this trains a general path-planning model for 10 × 10 to 80 × 80 square and rectangular maps. The algorithm outputs the lowest-cost path while considering the cost and the shortest path without considering the cost. The FCNN model analyzes the grid map information and outputs two probability maps, which show the probability of each point in the lowest-cost path and the shortest path. Based on the probability maps, the actual optimal path is reconstructed by using the highest probability method. The proposed method has superior speed advantages over traditional algorithms. On test maps of different sizes and shapes, for the lowest-cost path and the shortest path, the average optimal rates were 72.7% and 78.2%, the average success rates were 95.1% and 92.5%, and the average length rates were 1.04 and 1.03, respectively.
Open Access
Evaluation and selection of video stabilization techniques for UAV-based active infrared thermography application
(MDPI, 2021-02-25) Pant, Shashank; Nooralishahi, Parham; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Genest, Marc; Deane, Shakeb; Valdes, Julio J.; Zolotas, Argyrios; Maldague, Xavier P. V.
nmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV’s unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV’s unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.
Open Access
Facilitating autonomous systems with AI-based fault tolerance and computational resource economy
(MDPI, 2020-05-11) Deliparaschos, Kyriakos M.; Michail, Konstantinos; Zolotas, Argyrios
Proposed is the facilitation of fault-tolerant capability in autonomous systems with particular consideration of low computational complexity and system interface devices (sensor/actuator) performance. Traditionally model-based fault-tolerant/detection units for multiple sensor faults in automation require a bank of estimators, normally Kalman-based ones. An AI-based control framework enabling low computational power fault tolerance is presented. Contrary to the bank-of-estimators approach, the proposed framework exhibits a single unit for multiple actuator/sensor fault detection. The efficacy of the proposed scheme is shown via rigorous analysis for several sensor fault scenarios for an electro-magnetic suspension testbed.
Open Access
Fault detection in aircraft flight control actuators using support vector machines
(MDPI, 2023-02-02) Grehan, Julianne; Ignatyev, Dmitry; Zolotas, Argyrios
Future generations of flight control systems, such as those for unmanned autonomous vehicles (UAVs), are likely to be more adaptive and intelligent to cope with the extra safety and reliability requirements due to pilotless operations. An efficient fault detection and isolation (FDI) system is paramount and should be capable of monitoring the health status of an aircraft. Historically, hardware redundancy techniques have been used to detect faults. However, duplicating the actuators in an UAV is not ideal due to the high cost and large mass of additional components. Fortunately, aircraft actuator faults can also be detected using analytical redundancy techniques. In this study, a data-driven algorithm using Support Vector Machine (SVM) is designed. The aircraft actuator fault investigated is the loss-of-effectiveness (LOE) fault. The aim of the fault detection algorithm is to classify the feature vector data into a nominal or faulty class based on the health of the actuator. The results show that the SVM algorithm detects the LOE fault almost instantly, with an average accuracy of 99%.
Open Access
Feasible, robust and reliable automation and control for autonomous systems
(MDPI, 2022-07-07) Hamid, Umar Zakir Abdul; Hu, Chuan; Zolotas, Argyrios
Open Access
Fusion insights from ultrasonic and thermographic inspections for impact damage analysis
(AIAA, 2023-06-08) Torbali, M. Ebubekir; Alhammad, Muflih; Zolotas, Argyrios; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Maldague, Xavier
Low energy impact damage in composite materials may be more concerning than it appears visually, often requiring a detailed examination for accurate assessment to ensure safe and sustainable operation. Non-destructive testing (NDT) methods provide such inspection techniques, and in this paper, NDT-based fusion is explored for enhanced identification of defect size and location compared to indepdently using individual NDT methods separately. Three Carbon Fiber Reinforced Polymer (CFRP) specimens are examined, each with an impact damage of a given energy level, using pulsed thermography (PT) and phased array (PA) ultrasonic methods. Following the extraction of binary defect shapes from source images, a decision-level fusion approach is performed. The results indicate that combining ultrasonic and infrared thermography (IRT) inspections for CFRP composite materials is promising to achieve enhanced and improved detection traceability.
Open Access
GNSS/INS/VO fusion using gated recurrent unit in GNSS denied environments
(AIAA, 2023-01-19) Negru, Sorin A.; Geragersian, Patrick; Petrunin, Ivan; Zolotas, Argyrios; Grech, Raphael
Urban air mobility is a growing market, which will bring new ways to travel and to deliver items covering urban and suburban areas, at relatively low altitudes. To guarantee a safe and robust navigation, Unmanned Aerial Vehicles should be able to overcome all the navigational constraints. The paper is analyzing a novel sensor fusion framework with the aim to obtain a stable flight in a degraded GNSS environment. The sensor fusion framework is combining data coming from a GNSS receiver, an IMU and an optical camera under a loosely coupled scheme. A Federated Filter approach is implemented with the integration of two GRUs blocks. The first GRU is used to increase the accuracy in time of the INS, giving as output a more reliable position that it is fused, with the position information coming from, the GNSS receiver, and the developed Visual Odometry algorithm. Further, a master GRU block is used to select the best position information. The data is collected using a hardware in the loop setup, using AirSim, Pixhawk and Spirent GSS7000 hardware. As validation, the framework is tested, on a virtual UAV, performing a delivery mission on Cranfield university campus. Results showed that the developed fusion framework, can be used for short GNSS outages.
Open Access
Group design project in control engineering: Adapting to COVID-19 pandemic
(Elsevier, 2021-11-18) Ignatyev, Dmitry I.; Shin, Hyosang; Zolotas, Argyrios; Tsourdos, Antonios
Group Design Project (GDP) is a common education strategy in engineering. However, due to the COVID-19 pandemic, GDP cannot be fulfilled in a typical lab condition. The paper describes an example of delivering intensive hands-on, group project-based engineering course Autonomous Vehicle Dynamics and Control at Cranfield University. The project was designed to be implemented using modern simulation tools. As a result, students have not only obtained a better understanding of the engineering areas but also learned the usage of essential engineering and IT tools. The students obtained skillsets useful in modern engineering applications, where a simulation environment could improve the quality of the system before deployment and reduce a development cost.
Open Access
Hybrid terrain traversability analysis in off-road environments
(IEEE, 2022-03-22) Leung, Tiga Ho Yin; Ignatyev, Dmitry I.; Zolotas, Argyrios
There is a significant growth in autonomy level in off-road ground vehicles. However, unknown off-road environments are often challenging due to their unstructured and rough nature. To find a path that the robot can move smoothly to its destination, it needs to analyse the surrounding terrain. In this paper, we present a hybrid terrain traversability analysis framework. Semantic segmentation is implemented to understand different types of the terrain surrounding the robot; meanwhile geometrical properties of the terrain are assessed with the aid of a probabilistic terrain estimation. The framework represents the traversability analysis on a robot-centric cost map, which is available to the path planners. We evaluated the proposed framework with synchronised sensor data captured while driving the robot in real off-road environments. This thorough terrain traversability analysis will be crucial for autonomous navigation systems in off-road environments.
Open Access
H∞ mixed sensitivity optimization for high speed tilting trains
(Universitas Ahmad Dahlan, 2020-04-13) Hassan, Fazilah; Zolotas, Argyrios ; Mohd Shah, Shaharil
The industrial norm of tilting high speed trains, nowadays, is that of Precedence tilt (also known as Preview tilt). Precedence tilt, although succesfull as a concept, tends to be complex (mainly due to the signal interconnections between vehicles and the advanced signal processing required for monitoring). Research studies of early prior to that of precedence tilt schemes, i.e. the so-called Nulling-type schemes, utilized local-per-vehicle signals to provide tilt action (this was essentially a typical disturbance rejection-scheme) but suffered from inherent delays in the control). Nulling tilt may still be seen as an important research aim due to the simple nature and most importantly due to the more straightforward fault detection compared to precedence schemes. The work in this paper presents a substantial extension conventional to robust H∞ mixed sensitivity nulling tilt control in literature. A particular aspect is the use of optimization is used in the design of the robust controller accompanied by rigorous investigation of the conflicting deterministic/stochastic local tilt trade-off.

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