Software-in-the-loop Research Articles

Hybrid Testing System Development for Single Point Mooring Lines FOWT’s

Abstract To advance the development of various concepts for floating offshore wind turbines, it is imperative to have access to experimental data. These data are used in the validation of the tools for the simulation of floating turbines and also for the tuning of the models. CENER has developed a hybrid testing methodology that enhances the performance of wave basin tests for scaled models of Floating Offshore Wind Turbines (FOWTs). This approach allows us to apply forces and moments from the wind turbine to the floating platform without needing to replicate wind within the testing facility or create a complex model of the wind turbine rotor. However, when it comes to testing single point moored (SPM) platforms, a unique challenge arises. These platforms have an additional degree of freedom as they can freely rotate around the vertical axis of their mooring attachment (yaw degree of freedom). These type of platforms can present important misalignments with respect to the wind depending on the combination of wind, wave and current directions. Consequently, accurately capturing the impact of misalignment on platform dynamics, especially in yaw, is essential to assess the platform’s ability to self-align with the wind direction, a key factor known as weathervaning capacity. The X1Wind platform is an innovative floating wind system, based on a single point mooring solution with a downwind wind turbine configuration that allows it to be passively self-orientated. The concept has been extensively tested on prototypes at real sea conditions as well as on scaled models at wave basins. For the last wave basin testing campaign, X1Wind has entrusted CENER with the development of the hybrid testing methodology adapted to the requirements of SPM platforms. The testing campaign has covered different wave and wind conditions, including misalignment scenarios and has shown a good dynamic behavior of the platform on yaw. This study outlines the advancements made in the CENER Software-in-the-Loop (SiL) hybrid testing system for wave basin tests of SPM platforms. In order to correctly introduce the direction of the aerodynamic forces on the wind turbine under misaligned conditions, improvements on the software and hardware (loads actuator) of the SiL system were developed and tested. An actuator system consisting of 6 propellers was used. The numerical model that calculates the rotor aerodynamic loading were expanded to take into consideration the direction of the aerodynamic loads with respect to the platform. Additionally, the drag of the wind over the tower and other platform elements must be considered, as they can play a relevant role in the generation of the weathervaning moment. The study concludes that the use of the upgraded SiL hybrid testing system is an effective and afordable solution for the testing of scaled SPM platforms at wave basin facilities. It also summarizes the different considerations that the system has to overcome for the particularity of this type of testing application.

A graphics-based digital twin (GBDT) framework for accurate UAV localization in GPS-denied environments

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) is crucial for effective assessment of large-scale civil infrastructure, as manual UAV control is time consuming and prone to mishaps. Autonomous navigation outdoors typically employs GPS signals to enable accurate localization and reduce long-term drifts. However, in many civil engineering applications, GPS signals are either poor or unavailable due to interference and/or multi-path effects. Current approaches for UAV localization in GPS-denied environments, track geometric features such as corners; however, these natural features can be misrepresented due to the presence of occlusions and/or image processing errors, resulting in significant localization errors that can grow with time. AprilTags can reduce localization errors, but installation on large-scale civil infrastructure can be challenging. Therefore, this paper proposes a framework that leverages the wealth of visual and geometric information encoded in a Graphics-Based Digital Twin (GBDT) of a target infrastructure to provide accurate localization of a UAV. The GBDT is comprised of a computer graphics model that faithfully represents a target structure’s geometry, structural features, and visual textures. The visual details of the GBDT are exploited to design an object recognition algorithm that detects GBDTtags, which are distinctive objects (or a collection of components) on the target structure in advance; GBDTtags provide functionality similar to AprilTags. When the camera attached to a UAV detects one or more GBDTtags, the vertices of the GBDTtags are mapped from the image plane into the GBDT coordinate system, allowing for the UAV to be localized. The framework, termed herein as GBDTpose, is first validated numerically using Blender, Gazebo, and Mavros software-in-the-loop (SITL). Subsequently, field validation is carried out using the Kavita and Lalit Bahl Smart Bridge at the University of Illinois Urbana-Champaign (UIUC). Results show that localization in GPS-denied environments can be achieved with 5-50 cm accuracy without the need for physical markers being placed on the structure.

UAV p­­­arcel Delivery System with Deep Reinforcement Learning Based Collision Avoidance and Route Optimization

Unmanned Aerial Vehicles (UAVs), or drones, have recently become a favorable solution for fast parcel delivery due to their maneuverability and advances in navigation technologies. With the limitation of battery capacity and payload of drones, it is crucial to consider both efficiency and cost while conducting the tasks. Meanwhile, UAVs should not collide with each other while traveling to customers. In this article, we propose a UAV parcel delivery system involving deep reinforcement learning (DRL) approach for collision avoidance and a genetic algorithm for route optimization. Specifically, a delivery center generates near-optimal routes, loading UAV with parcels according to demands. Each UAV takes charge of delivering packages in compliance with the assigned route while avoiding collision with each other. We utilize DRL to achieve collision avoidance without having prior knowledge about the trajectories of other UAVs. Additionally, we adopt a genetic algorithm to obtain the lowest energy cost path for each UAV. To find such an optimized path, we solve a capacitated vehicle routing problem (CVRP) with a modified cost function and extra constraints. Realistic simulations using a physics engine and software-in-the-loop (SITL) are conducted to evaluate the feasibility of the proposed methods.

Event-Triggered Collaborative Fault Diagnosis for UAV–UGV Systems

The heterogeneous unmanned system, which is composed of unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV), has been broadly applied in many domains. Collaborative fault diagnosis (CFD) among UAVs and UGVs has become a key technology in these unmanned systems. However, collaborative fault diagnosis in unmanned systems faces the challenges of the dynamic environment and limited communication bandwidth. This paper proposes an event-triggered collaborative fault diagnosis framework for the UAV–UGV system. The framework aims to achieve autonomous fault monitoring and cooperative diagnosis among unmanned systems, thus enhancing system security and reliability. Firstly, we propose a fault trigger mechanism based on broad learning systems (BLS), which utilizes sensor data to accurately detect and identify faults. Then, under the dynamic event triggering mechanism, the network communication topology between the UAV–UGV system and BLS is used to achieve cooperative fault diagnosis. To validate the effectiveness of our proposed scheme, we conduct experiments on a software-in-the-loop (SIL) simulation platform. The experimental results demonstrate that our method achieves high diagnosis accuracy for the UAV–UGV system.

Multidimensional hybrid software-in-the-loop modeling approach for experimental analysis of a floating offshore wind turbine in wave tank experiments

A hybrid experimental modeling approach for floating offshore wind turbines (FOWTs) in a wave tank is presented. The method called real-time hybrid method (RTHM) or software-in-the-loop (SIL) includes physically reproduced hydrodynamic loads in the wave tank, with a Froude-scaled model while aerodynamic loads are reproduced on the model by an actuator. The force is applied on the tower top and calculated by a numerical simulation running in real-time as a function of the measured velocity and position of the physical model. Couplings between the platform motion, the aerodynamic loads and the wind turbine controller are hence accounted for. A new SIL actuator enabling the reproduction of a multi-component force at the tower top of an experimental scaled wind turbine model is developed, and the quantification of the accuracy of the replicated forces is studied. The test case includes a floating spar platform carrying a 10 MW horizontal axis wind turbine. The results show that this SIL actuator can accurately replicate the low and wave-frequency aerodynamic loads, which are essential for wave tank testing. Lastly, a case study of the wind turbine model in colinear and misaligned wind and wave conditions is presented, showing 3D forces and induced motions.

Advancing Artificial Intelligence (AI) and Machine Learning (ML) Based Soft Sensors for In-Cylinder Predictions with a Real-Time Simulator and a Crank Angle Resolved Engine Model

In a previous research effort by this group, pseudo engine dynamometer data in multi-dimensional arrays were combined with dynamic equations to form a crank angle resolved engine model compatible with a real-time simulator. The combination of the real-time simulator and external targets enabled the development of a software-in-the-loop (SIL) environment that enabled near-real-time development of AI/ML and real-time deployment of the resulting AI/ML. Military applications, in particular, are unlikely to possess large quantities of non-sparse operational data that span the full operational range of the system, stove-piping the ability to develop and deploy AI/ML which is sufficient for near-real-time or real-time control. AI/ML has been shown to be well suited for predicting highly non-linear mathematical phenomena and thus military systems could potentially benefit from the development and deployment of AI/ML acting as soft sensors. Given the non-sparse nature of the data, it becomes exceedingly important that AI/ML be developed and deployed in near-real-time or real-time in parallel to a real-time system to overcome the inadequacy of applicable data. This research effort used parallel processing to reduce the training duration of the shallow artificial neural networks (SANN) and forest algorithms forming ensemble models. This research is novel in that the SIL environment enables pre-developed AI/ML to be adapted in near-real-time or develop AI/ML in response to changes within the operation of the applied system, different load torques, engine speeds, and atmospheric conditions to name a few. Over time it is expected that the continued adaptation of the algorithms will lead to the development of AI/ML that is suitable for real-time control and energy management.

Evaluation of SiL Testing Potential—Shifting from HiL by Identifying Compatible Requirements with vECUs

Due to the increasing complexity of vehicle software, it is becoming increasingly difficult to comprehensively test all requirements. This inevitably means that alternative test methods, e.g., simulation-based methods, must be used more frequently. However, the challenge involves identifying appropriate requirements that can be technically tested in a simulation environment initially. The present work is aimed at evaluation and optimization of the effectiveness of software-in-the-loop (SiL) simulations in the testing process of vehicle software. The focus is on supporting the testing process by shifting specific test cases from hardware-in-the-loop (HiL) test benches to SiL-based simulations. For this purpose, a systematic approach was developed to analyze and categorize requirements, enabling precise and efficient allocation of test cases. Furthermore, a detailed review and recommendation for improving the ProSTEP iViP standard for virtual electronic control units (vECU) was carried out. The developed matrix associates the defined requirement clusters with different classifications of vECUs, facilitating the identification of suitable test environment types for conducting specific test cases. By assigning test cases to appropriate vECU levels, the testing processes can be targeted and cost-optimized. Finally, the theoretical results were evaluated in an SiL simulation environment. It was observed that a significant part of the requirements could effectively be tested using a vECU. These findings confirmed the potential of SiL simulation environments to not only support, but also enhance, the testing process for vehicle software by providing a cost-effective and flexible complement to traditional HiL test benches.

Swarm of Drones in a Simulation Environment—Efficiency and Adaptation

In the swiftly advancing field of swarm robotics and unmanned aerial vehicles, precise and effective testing methods are essential. This article explores the crucial role of software-in-the-loop (SITL) simulations in developing, testing, and validating drone swarm control algorithms. Such simulations play a crucial role in reproducing real-world operational scenarios. Additionally, they can (regardless of the type of application) accelerate the development process, reduce operational risks, and ensure the consistent performance of drone swarms. Our study demonstrates that different geometrical arrangements of drone swarms require flexible control strategies. The leader-based control model facilitates coherent movement and enhanced coordination. Addressing various issues such as communication delays and inaccuracies in positioning is essential here. These shortcomings underscore the value of improved approaches to collision avoidance. The research described in this article focused on the dynamics of drone swarms in a simulated context and emphasized their operational efficiency and adaptability in various scenarios. Advanced simulation tools were utilized to analyze the interaction, communication, and adaptability of autonomous units. The presented results indicate that the arrangement of drones significantly affects their coordination and collision avoidance capabilities. They also underscore the importance of control systems that can adapt to various situations. The impact of communication delays and errors in positioning systems on the required distance between drones in a grid structure is also presented. This article assesses the impact of different levels of GPS accuracy and communication delays on the coordination of group movement and collision avoidance capabilities.

Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning

In the realm of Unmanned Aerial Vehicles (UAVs) for civilian applications, the surge in demand has underscored the need for sophisticated technologies. The integration of Unmanned Aerial Systems (UAS) with Artificial Intelligence (AI) has become paramount to address challenges in urban environments, particularly those involving obstacle collision risks. These UAVs are equipped with advanced sensor arrays, incorporating LiDAR and computer vision technologies. The AI algorithm undergoes comprehensive training on an embedded machine, fostering the development of a robust spatial perception model. This model enables the UAV to interpret and navigate through the intricate urban landscape with a human-like understanding of its surroundings. During mission execution, the AI-driven perception system detects and localizes objects, ensuring real-time awareness. This study proposes an innovative real-time three-dimensional (3D) path planner designed to optimize UAV trajectories through obstacle-laden environments. The path planner leverages a heuristic A* algorithm, a widely recognized search algorithm in artificial intelligence. A distinguishing feature of this proposed path planner is its ability to operate without the need to store frontier nodes in memory, diverging from conventional A* implementations. Instead, it relies on relative object positions obtained from the perception system, employing advanced techniques in simultaneous localization and mapping (SLAM). This approach ensures the generation of collision-free paths, enhancing the UAV's navigational efficiency. Moreover, the proposed path planner undergoes rigorous validation through Software-In-The-Loop (SITL) simulations in constrained environments, leveraging high-fidelity UAV dynamics models. Preliminary real flight tests are conducted to assess the real-world applicability of the system, considering factors such as wind disturbances and dynamic obstacles. The results showcase the path planner's effectiveness in providing swift and accurate guidance, thereby establishing its viability for real-time UAV missions in complex urban scenarios.

Comparison of Flight Parameters in SIL Simulation Using Commercial Autopilots and X-Plane Simulator for Multi-Rotor Models

Modern aviation technology development heavily relies on computer simulations. SIL (Software-In-The-Loop) simulations are essential for evaluating autopilots and control algorithms for multi-rotors, including drones and other UAVs (Unmanned Aerial Vehicle). In such simulations, it is possible to compare the flight parameters achieved by flying platforms using various commercial autopilots widely used in the UAV sector. This research aims to provide objective and comprehensive insights into the effectiveness of different autopilot systems This article examines the simulated flight test results of a drone performing the same mission using different autopilot systems. The X-Plane software was used as an environment to simulate the dynamics of the drone and its surroundings. Matlab/Simulink r2023a provided the interface between autopilot software and X-Plane models. Those methods allowed us to obtain an appropriate comparison of the autopilot systems and indicate the main differences between them. This research focused on analyzing UAV flight characteristics such as stability, trajectory tracking, response time to control changes, and the overall effectiveness of autopilots. Various flight scenarios including take-off, landing, flight at a constant altitude, dynamic manoeuvrers and, flight along a planned trajectory were also examined. In order to obtain the most accurate and realistic results, the tests were carried out in various weather conditions. The aim of this research is to provide objective data and analysis to compare the performance of commercial autopilots. This method offers several advantages, including cost-effective testing, the ability to test in diverse environmental conditions, and the evaluation of autopilot algorithms without the need for real hardware. The findings of this study may have a considerable impact on how autopilot designers and developers choose the best platforms and technologies for their projects. Future research on this topic will compare the obtained data with flight test data.

Comparison of Multiple Models in Decentralized Target Estimation by a UAV Swarm

The decentralized estimation and tracking of a mobile target performed by a group of unmanned aerial vehicles (UAVs) is studied in this work. A flocking protocol is used for maintaining a collision-free formation, while a decentralized extended Kalman filter in the information form is employed to provide an estimate of the target state. In the prediction step of the filter, we adopt and compare three different models for the target motion with increasing levels of complexity, namely, a constant velocity (CV), a constant turn (CT), and a full-state (FS) model. Software-in-the-loop (SITL) simulations are conducted in ROS/Gazebo to compare the performance of the three models. The coupling between the formation and estimation tasks is evaluated since the tracking task is affected by the outcome of the estimation process.

MIL, SIL and PIL Implementation for Closed Loop Control of Flyback Converter

Power electronic systems are rapidly evolving; thus, effective prototyping methods are required to test control algorithms and assess system performance prior to hardware implementation. This research suggests a Model-In-The-Loop (MIL), Software-In-The-Loop (SIL), and Processor-In-The-Loop (PIL) methodologies-based complete prototyping strategy for flyback converters using the TI F28069M Launchpad kit. These techniques can be combined to evaluate control strategies accurately and quickly, speeding up design cycles and enhancing system reliability. The proposed prototyping platform is presented in this work, along with a thorough explanation of each prototyping stage and its associated advantages. The effectiveness of the suggested approach for 50W rated power flyback converter in terms of quick algorithm development, system simulation, real-time control implementation and controller design accuracy is analyzed and shown by experimental results. The results show that the performance of the designed controller for the flyback converter is almost the same as in MIL and SIL implementation in terms of the overshoot and settling time in the reference voltage tracking. On the other hand, in the PIL implementation, the overshoot performance of the controller deviates by 1.18% more than in SIL and MIL implementation. These also confirm that the flyback control system's performance is reliable and effective during all phases of development.

SIL Setup for Motor Controller Testing in Virtual Vehicle Environments

This research paper explores the implementation and benefits of Software-in-the-Loop (SIL) setups for testing motor controllers within virtual vehicle environments. SIL testing offers significant advantages in terms of cost, efficiency, and safety compared to traditional hardware-based testing methods. This paper discusses the key components of SIL setups, their implementation strategies, and case studies demonstrating their effectiveness in real-world applications. Additionally, challenges and future research directions in SIL testing for motor controllers are also addressed.

Analysis of UTM Tracking Performance for Conformance Monitoring via Hybrid SITL Monte Carlo Methods

Conformance monitoring supports UTM safety by observing if unmanned aircraft (UA) are adhering to declared operational intent. As a supporting system, robust cooperative tracking is critical. Nevertheless, tracking systems for UAS traffic management (UTM) are in an early stage and under-standardized, and existing literature hardly addresses the problem. To bridge this gap, this study aims to probabilistically evaluate the impact of the change in tracking performances on the effectiveness of conformance monitoring. We propose a Monte Carlo simulation-based method. To ensure a realistic simulation environment, we use a hybrid software-in-the-loop (SITL) scheme. The major uncertainties contributing to the stochastic evaluation are measured separately and are integrated into the final Monte Carlo simulation. Latency tests were conducted to assess the performance of different communication technologies for cooperative tracking. Flight technical error generation via SITL simulations and navigational system error generation based on flight experiments were employed to model UA trajectory uncertainty. Based on these tests, further Monte Carlo simulations were used to study the overall impacts of various tracking key performance indicators in UTM conformance monitoring. Results suggest that the extrapolation of UA position enables quicker non-conformance detection, but introduces greater variability in detection delay, and exacerbates the incidence of nuisance alerts and missed detections, particularly when latencies are high and velocity errors are severe. Recommendations for UA position update rates of ≥1 Hz remain consistent with previous studies, as investments in increasing the update rate do not lead to corresponding improvements in conformance monitoring performance according to simulation results.

Development of a real-time framework between MATLAB and PLC through OPC-UA: A case study of a microgrid energy management system

Numerous research laboratories engaged in developing advanced algorithms for controlling Renewable Energy Systems (RES) face constraints due to the absence of specialized equipment for conducting research and experiments. Real-time simulation platforms are well-suited for creating fast or real-time computation models, offering prompt, accurate, and reliable insights for RES control implementation. Nevertheless, the high cost of real-time platforms renders them unaffordable for many laboratories. To address this issue, a cost-effective platform is designed to assist researchers in their activities. The hardware solution involves utilizing a Programmable Logic Control (PLC) to acquire data from diverse sensors and regulate the system through actuators and PI controllers. From a software perspective, a communication framework between MATLAB and the PLC is developed using OPC-UA (Open Platform Communications Unified Architecture) technology. In this paper, the operation and efficiency of the platform have been tested using the Software in the Loop (SIL) technique. The operating model is designed using MATLAB/Simulink to simulate and analyze the appropriate control technique suitable for PLC implementation, whereas the control is executed through Siemens S7–1200 PLC using TIA PORTAL. A visualization interface is established using WinCC-RT. Moreover, this paper presents the platform's design, discusses its application, and illustrates its usage through a case study. Indeed, a complex algorithm developed in MATLAB/Simulink can be automatically executed by any PLC using the designed framework.

Software Rejuvenation for Safe Operation of Cyber–Physical Systems in the Presence of Run-Time Cyberattacks

Software rejuvenation refers to the periodic restoration of a computing system’s software and data with a previously saved image to eliminate undesired behavior due to unknown or unanticipated conditions in the computing system. Researchers have recently proposed software rejuvenation as a defense against run-time cyberattacks that can modify the code and data in controllers for cyber–physical systems by restoring the run-time system with images that are free of malicious modifications. Motivated by the theory developed to design CPS software rejuvenation using invariant sets and reachability analysis, this article presents an empirical approach to determine the timing and mode-switching conditions, as well as a run-time platform to enhance existing CPS controllers with software rejuvenation. The results are demonstrated using real-time software-in-the-loop (SITL) simulation of a quadrotor with the widely used PX4 controller.

A Drone Secure Handover Architecture validated in a Software in the Loop Environment

The flight and control capabilities of uncrewed aerial vehicles (UAVs) have increased significantly with recent research for civilian and commercial applications. As a result, these devices are becoming capable of flying ever greater distances, accomplishing flights beyond line of sight (BVLOS). However, given the need for safety guarantees, these flights are increasingly subject to regulations. Handover operations between controllers and the security of the exchanged data are a challenge for implementing these devices in various applications. This paper presents a secure handover architecture between control stations, using a Software in the Loop (SIL) model to validate the adopted strategies and mitigate the time between simulation and real systems implementations. This architecture is developed in two separate modules that perform the security and handover processes. Finally, we validate the proposed architecture with several drone flights on a virtual testbed.

Software-In-The-Loop validation of super twisting based sliding mode control for quadcopters

This research paper proposes a super twisting-based sliding mode controller for quadcopter's attitude and position tracking in the presence of uncertainties. In the design of a super twisting-based sliding mode controller, the PID sliding surface is taken into account, which can improve the tracking performance. The Lyapunov-based stability analysis has been shown to ensure finite time stability of the sliding variables, and then the convergence of error trajectories has been discussed. The designed controller has been validated by simulations in MATLAB and Software-In-The-Loop (SITL) simulations in a ROS-Gazebo based environment simulator for the Iris model. The simulations demonstrate the robustness of the proposed controller to different wind disturbances.

Validation of automotive centrals using hardware in the loop -BCM and Lights

The race for electrification and the need for innovation to attract customers has driven the automotive industry to do something different in vehicles, emerging emission control challenges and efficient technological availability are the pillars of innovation. With the need to upgrade industrial manufacturing systems, directly impacting vehicle production, established the search for new prototyping methodologies and use of virtual tools for testing, validation of components and vehicle systems are developed. The demand for electronic components controls units (ECU) is growing, due to the availability of intelligence and security in today's vehicles, directly impacting their development and the performance and functionality tests. To meet this new reality, in addition to the automotive test prototypes used during development, the automotive industry has been using different virtual environments to produce, verify, and validate its vehicles. Therefore, this paper applied integration and validation using the HIL test platform, focusing on the ECU Body Control Unit (BCM), analyzing its functionalities and possible failures of the internal and external light loads. After, a brief commentary will be made exposing other test medium platforms given as the Plywood Buck (PWB), checking the reliability, flexibility, installation time, and costs of the three test platforms Software in the loop (SIL), Model in the loop (MIL) , Hardware in the loop (HIL), in order to verify their advantages, challenges, and problems in the use and information to optimize the use of each platform and test medium.

Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method

The number of Unmanned Aerial Vehicles (UAVs) used in various industries has increased exponentially, and abnormal detection of UAVs is one of the primary technical means to ensure that UAVs can work normally. Currently, most anomaly detection models are trained using on-board logs from drones. However, in some cases, using these logs can be problematic due to data encryption, inconsistent descriptions of characteristics, and imbalanced positive and negative samples. Consequently, the on-board logs of UAVs may not be directly usable for training anomaly detection models. Given the above problems, this paper proposes a Time Line Modeling (TLM) method based on the UAV software-in-the-loop (SITL) simulation environment to obtain and process the on-board failure logs of drones. The Time Line Modeling method includes two stages: the Fault Time Point Anchoring Method and Fault Time Window Stretching Method. First, based on the SITL simulation environment, multiple flight missions were constructed. Failures of several common components of UAVs are designed. Secondly, the fault’s initial location and end location are determined by the method of Fault Time Point Anchoring, and the original collection of tagged UAV’s on-board data is realized. Then, in terms of data processing, the features that are not universal are removed, and the flight data of the UAV is optimized by using the data balance method of Time Window Stretching to achieve the balance of normal data and abnormal data. Finally, use of algorithms such as Sequential Minimal Optimization (SMO), Random Forest (RF), and Convolutional Neural Network (CNN) were used to experiment with the processed data. The experimental results showed that the data set obtained based on this method can be effectively applied to the training of machine learning-based anomaly detection models.