Software-in-the-loop Research Articles

Validation of Automotive Centrals Using Hardware in the Loop-Body Control Unit and Lights

The race for electrification and the need for innovation to attract customers has led the automotive industry to do something different with vehicles. New emissions control challenges and efficient technological availability are the pillars of creation. The growing demand to upgrade industrial manufacturing systems creates actions that directly impact vehicle production. With this comes the search for new prototyping methods and virtual tools for component testing and validation, and vehicle systems have established themselves. The demand for Electronic Control Units (ECU) is increasing due to the availability of intelligence and safety in today's vehicles, directly affecting their development, performance, and functional testing. In order to keep up with global changes, the automotive industry uses different virtual environments to produce, verify and validate their vehicles and test prototypes used during development. Therefore, in this paper, integration and validation were performed using the Hardware in the Loop (HIL) test platform, focusing on the ECU Body Control Unit (BCM). Then, a brief commentary reviews other test medium platforms, such as the Plywood Buck (PWB), and examines the reliability, flexibility, installation time, and cost of the three test platforms, software in the loop (SIL), Model in the loop (MIL), and HIL, to review their benefits, challenges, and issues in use and information to optimize the use of each platform and test medium.

GAS turbine sensor fault diagnostic system in a real-time executable digital-twin

In this study, a sensor fault diagnostic system to detect/isolate and accommodate faults in sensors from an industrial gas turbine has been developed. The sensor fault diagnostic module is integrated with a gas turbine real-time executable digital-twin (RT xDT) reported in a previous study. The sensor fault diagnostic module of the digital-twin considers analytical sensor redundancy using a reference engine model to provide redundant estimates of measured engine variables. A Software-in-the-Loop (S-i-L) architecture and Hardware-in-the-Loop (H-i-L) facility are constructed to assess the sensor diagnostic module (fault detection/ fault isolation) during failure in sensors from the engine. The results demonstrated that if the discrepancy between virtual measurement (provided by digital-twin) and sensor measurement exceeds the prescribed tolerance levels, the sensor fault diagnostic logic determines the state of switching between the virtual and engine sensor measurements in a dual lane control configuration of the gas turbine control system. The sensor fault detection system implemented in the gas turbine RT xDT can be deployed onto a distributed control system of industrial gas turbines to diagnose sensor deficiencies and ensure continuous and safe operation of the gas turbine. Consequently, the developed system will increase engine availability and reliability by diagnosing engine operational deficiencies before severe failure.

Visual based GNC system from prototype to flight software

Juventas is a 6U CubeSat which is part of HERA planetary defense mission and takes advantage of an innovative visual based guidance, navigation and control (GNC) system to perform autonomous navigation in the proximity of the Didymain system. The GNC system is designed to ensure safe navigation in the harsh and unpredictable environment of deep space and finally to take more risks by landing on the surface of Dimorphos. To achieve a qualitative software (SW) product, a dedicated procedure of SW lifecycle is developed by starting with GNC and image processing design, which concludes with the final embedded system that will perform the visual navigation task. A Design, Development, Verification and Validation (DDVV) approach is developed to achieve the flight software: model in the loop (MIL), auto-coding, software in the loop (SIL), processor in the loop (PIL) and finally hardware in the loop (HIL). The DDVV is developed by having as guideline the ECSS standards for SW. The flight software reaches maturity by performing dynamic/static code analysis and code coverage. To ensure an optimal process, a waterfall life-cycle is considered, where dedicated MIL, SIL, PIL and HIL test benches are developed to fully support the activity and to reduce to minimum the development costs.[7]

State-Dependent Riccati Equation-Based UAV Path Following Guidance for Shipborne Net Recovery

The landing of a fixed-wing aircraft on a ship is a challenging task due to a constrained landing space, wind disturbances, and the ship motion. In this paper, we consider the landing problem as a moving path following problem and present a decoupled three-dimensional State-Dependent Riccati Equation (SDRE)-based guidance law for net recovery landing of a fixed-wing unmanned aerial vehicle (UAV) on board a ship deck. Stability analysis of the guidance law is carried out and its performance is evaluated on aerosonde UAV in MATLAB and RASCAL aircraft in software-in-the-loop (SITL) taking actuator dynamics, different sea states for ship motion and varying wind disturbances into account. The proposed approach shows the general applicability of the guidance law for different types of aircraft

An IoT-based energy management system for AC microgrids with grid and security constraints

This paper proposes an Internet-of-Things (IoT) based energy management system (EMS) for the optimal operation of unbalanced three-phase AC microgrids. The system utilizes a software architecture based on microservices, which includes a stochastic economic dispatch optimizer (EDO), a database, a web-based graphical user interface (GUI), and an application programming interface (API). The EDO uses a mixed-integer linear programming (MILP) model to ensure the day-ahead dispatch of the distributed energy resources (DERs) in the microgrid while adhering to grid constraints such as voltage, current, and power limits. Additionally, the optimization module takes into account security constraints for unplanned islanded operation, as well as stochastic scenarios of local demand and renewable generation. To assess the performance of the proposed IoT-based EMS, tests are conducted using a real-time simulator in a software-in-the-loop (SIL) experimental setup. Actual data from a microgrid located at the State University of Campinas (UNICAMP) in Brazil is utilized for the tests. The microgrid consisted of a photovoltaic (PV) system, a battery energy storage system (BESS), a thermal generation unit, and variable demands. Results indicated the effectiveness of the proposed IoT-based EMS in monitoring the operation of the microgrid and defining the optimal day-ahead dispatch of local DERs.

Camera-Based Local and Global Target Detection, Tracking, and Localization Techniques for UAVs

Multiple-object detection, localization, and tracking are desirable in many areas and applications, as the field of deep learning has developed and has drawn the attention of academics in computer vision, having a plethora of networks now achieving excellent accuracy in detecting multiple objects in an image. Tracking and localizing objects still remain difficult processes which require significant effort. This work describes an optical camera-based target detection, tracking, and localization solution for Unmanned Aerial Vehicles (UAVs). Based on the well-known network YOLOv4, a custom object detection model was developed and its performance was compared to YOLOv4-Tiny, YOLOv4-608, and YOLOv7-Tiny. The target tracking algorithm we use is based on Deep SORT, providing cutting-edge tracking. The proposed localization approach can accurately determine the position of ground targets identified by the custom object detection model. Moreover, an implementation of a global tracker using localization information from up to four UAV cameras at a time. Finally, a guiding approach is described, which is responsible for providing real-time movement commands for the UAV to follow and cover a designated target. The complete system was evaluated in Gazebo with up to four UAVs utilizing Software-In-The-Loop (SITL) simulation.

Designing MPC algorithms for velocity control of brushed DC motor and verification with SIL tests

The model predictive control (MPC) tries to find the best control output by optimizing among the predictions. In this context, determining the objective function of optimization is a critical process. This study discusses the velocity control of the mini brushed direct current (BDC) motor used in the actuator with MPC. Four different functions have been defined to design the most appropriate cost function for MPC by considering the parameters that may be effective in the motor dynamics such as velocity, current, power, and switching states. Software in the Loop (SIL), one of the modeling-based testing methods, was used within the scope of testing the algorithms. After the SIL tests, it has been observed that the mini BDC motor can be successfully controlled with simple adjustments to the cost function of the MPC. As a result of the analysis obtained the best results with the objective function consisting of velocity error, estimated current, and the difference between the two estimated velocity values. With this controller, the BDC motor can be controlled without overshoot and with a steady-state error of less than 2% under load.

Enhancement of Heavy-Duty Engines Performance and Reliability Using Cylinder Pressure Information

Sustainability issues are becoming increasingly prominent in applications requiring the use of heavy-duty engines. Therefore, it is important to cut the emissions and costs of such engines to reduce the carbon footprint and keep the operating expenses under control. Even if for some applications a battery electric equipment is introduced, the diesel-equipped machinery is still popular thanks to the longer operating range. In this field, the open pit mines are a good example. In fact, the Total Cost of Ownership (TCO) of the mining equipment is highly impacted by fuel consumption (engine efficiency) and reliability (service interval and engine life). The present work is focused on efficiency enhancements achievable through the application of a combustion control strategy based on the in-cylinder pressure information. The benefits are mainly due to two factors. First, the negative effects of injectors aging can be compensated. Second, cylindrical online calibration of the control parameters enables the combustion system optimization. The article is divided into two parts. The first part describes the toolchain that is designed for the real-time application of the combustion control system, while the second part concerns the algorithm that would be implemented on the Engine Control Unit (ECU) to leverage the in-cylinder pressure information. The assessment of the potential benefits and feasibility of the combustion control algorithm is carried out in a Software in the Loop (SiL) environment, simulating both the developed control strategy and the engine behavior (Liebherr D98). Our goal is to validate the control algorithm through SiL simulations. The results of the validation process demonstrate the effectiveness of the control strategy: firstly, cylinder disparity on IMEP (+/−2.5% in reference conditions) is virtually canceled. Secondly, MFB50 is individually optimized, equalizing Pmax among the cylinders (+/−4% for the standard calibration) without exceeding the reliability threshold. In addition to this, BSFC is reduced by 1% thanks to the accurate cylinder-by-cylinder calibration. Finally, aging effects or fuel variations can be implicitly compensated, keeping optimal performance thorough the engine life.

Vision-Based Autonomous Following of a Moving Platform and Landing for an Unmanned Aerial Vehicle

Interest in Unmanned Aerial Vehicles (UAVs) has increased due to their versatility and variety of applications, however their battery life limits their applications. Heterogeneous multi-robot systems can offer a solution to this limitation, by allowing an Unmanned Ground Vehicle (UGV) to serve as a recharging station for the aerial one. Moreover, cooperation between aerial and terrestrial robots allows them to overcome other individual limitations, such as communication link coverage or accessibility, and to solve highly complex tasks, e.g., environment exploration, infrastructure inspection or search and rescue. This work proposes a vision-based approach that enables an aerial robot to autonomously detect, follow, and land on a mobile ground platform. For this purpose, ArUcO fiducial markers are used to estimate the relative pose between the UAV and UGV by processing RGB images provided by a monocular camera on board the UAV. The pose estimation is fed to a trajectory planner and four decoupled controllers to generate speed set-points relative to the UAV. Using a cascade loop strategy, these set-points are then sent to the UAV autopilot for inner loop control. The proposed solution has been tested both in simulation, with a digital twin of a solar farm using ROS, Gazebo and Ardupilot Software-in-the-Loop (SiL); and in the real world at IST Lisbon's outdoor facilities, with a UAV built on the basis of a DJ550 Hexacopter and a modified Jackal ground robot from DJI and Clearpath Robotics, respectively. Pose estimation, trajectory planning and speed set-point are computed on board the UAV, using a Single Board Computer (SBC) running Ubuntu and ROS, without the need for external infrastructure.

Computer-Aided Greenery Design—Prototype Green Structure Improving Human Health in Urban Ecosystem

Increasing population and urbanization, with climate change consequences, such as rising temperatures, influence public health and well-being. The search to improve the quality of life in cities becomes one of the priority objectives. A solution can be found in the role of greenery in an urban environment and its impact on human health. This opens a path toward experimentation on microclimate green structures that can be inserted into dense urban spaces providing human and environmental benefits. The article proposes an automated greenery design method combined with rapid prototyping for such interventions. A theoretical analysis of the problem preceded the introduction of the method. The research process was developed in accordance with the main objectives of the CDIO framework (Conceive, Design, Implement, and Operate) with the SiL (Software in the Loop) and HiL (Hardware in the Loop) methods. Moreover, the applied test model allows for complex evaluation in order to ensure quality and directions for further development.

DPM: Towards Accurate Drone Position Manipulation

While existing methods can disrupt drone invasions in a protected airspace, none of them is able to accurately guide a drone to a desired location for safe handling. To address this issue, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Drone Position Manipulation (DPM)</i> attack is proposed in this article to explore weaknesses in common navigation algorithms of consumer drones. The main advantage of DPM is that it can accurately redirect an invading drone to a desired location, by carefully crafting the spoofed guidance inputs of the drone to exploit the adjustment of path-following navigation algorithms. Compared with existing methods, this is the first work to achieve such accurate quantitative control. Another main contribution of this work is that it explores three fundamental components (i.e., guidance sensing, state estimation, and navigation control) together to enable the quantitative manipulation of flight paths, different from all existing methods. Furthermore, a formal analysis of the attack range is presented for investigating where a drone can be redirected from its target under given constraints. The evaluation on the Software-in-the-loop (SITL) module of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ArduPilot</i> system shows that the proposed attack is able to not only accurately lead a drone to a redirected destination but also make it fairly far away from its target. Lastly, the proposed attack can be applied to many autopiloted systems, because it exploits common weaknesses in these systems.

A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car

The automatic emergency braking (AEB) system of the passenger car is responsible for auxiliary braking judgment and decision-making in an emergency. Due to the inevitable pressure response delay of passenger car pneumatic braking systems, a large number of verification tests should be carried out to propose appropriate strategies and algorithms. To realize the rapid verification of the AEB control algorithm, a verification system integrating software-in-the-loop (SIL) and hardware-in-the-loop (HIL) was proposed for a two-axle passenger car. It can verify the logic feasibility of the control algorithm through SIL testing, and can verify the implementation effect of the control algorithm through HIL testing. The verification system is composed of IPG, dSPACE, and a pneumatic braking bench. Considering the influence of pneumatic braking delay, it is well-matched with the actual vehicle AEB system. The AEB hierarchical control algorithm was verified under three typical test conditions. The results show that the SIL testing results of speed and relative distance are in good agreement with the HIL testing results, and the average relative deviation of relative distance is only 1.7 m. The single test time of the SIL testing is about 228 s, which can meet the requirements of rapid verification of the AEB control algorithm of the passenger car.

Computer Vision Based Path Following for Autonomous Unmanned Aerial Systems in Unburied Pipeline Onshore Inspection

Unmanned Aerial Systems (UAS) are becoming more attractive in diverse applications due to their efficiency in performing tasks with a reduced time execution, covering a larger area, and lowering human risks at harmful tasks. In the context of Oil &amp; Gas (O&amp;G), the scenario is even more attractive for the application of UAS for inspection activities due to the large extension of these facilities and the operational risks involved in the processes. Many authors proposed solutions to detect gas leaks regarding the onshore unburied pipeline structures. However, only a few addressed the navigation and tracking problem for the autonomous navigation of UAS over these structures. Most proposed solutions rely on traditional computer vision strategies for tracking. As a drawback, depending on lighting conditions, the obtained path line may be inaccurate, making a strategy to force the UAS to continue on the path necessary. Therefore, this research describes the potential of an autonomous UAS based on image processing technique and Convolutional Neural Network (CNN) strategy to navigate appropriately in complex unburied pipeline networks contributing to the monitoring procedure of the Oil &amp; Gas Industry structures. A CNN is used to detect the pipe, while image processing techniques such as Canny edge detection and Hough Transform are used to detect the pipe line reference, which is used by a line following algorithm to guide the UAS along the pipe. The framework is assessed by a PX4 flight controller Software-in-The-Loop (SITL) simulations performed with the Robot Operating System (ROS) along with the Gazebo platform to simulate the proposed operational environment and verify the approach’s functionality as a proof of concept. Real tests were also conducted. The results showed that the solution is robust and feasible to deploy in this proposed task, achieving 72% of mean average precision on detecting different types of pipes and 0.0111 m of mean squared error on the path following with a drone 2 m away from a tube.

Optimal Sensor Placement for Multifault Detection and Isolation in Lithium-Ion Battery Pack

The work presented in this article is motivated by a project related to the electrification of commercial aircraft. Energy storage systems (ESSs) for hybrid-electric aircraft applications require the ability to provide accurate diagnoses to insure system availability and reliability. In aerospace applications, battery packs may consist of thousands of interconnected cells and the associated electrical/electronic hardware, which brings a series of challenges for designing the battery management system (BMS). This article uses the tools of structural analysis to determine the placement of sensors that are needed by the BMS to enable monitoring and fault diagnosis at the individual cell level. First, the degree of analytical redundancy (AR) in the battery system that can be used for diagnostic strategies is determined. Then, structural models of different battery pack architectures are used to study how different measurements (current, voltage, and temperature) may improve the ability to monitor and diagnose a battery system. Possible sensor placement strategies that would enable the diagnosis of individual sensor faults, individual cell faults, and connection faults for different battery pack topologies are analyzed as well. A software-in-the-loop (SIL) framework is utilized to validate the proposed approach.

Deep learning in the development of energy Management strategies of hybrid electric Vehicles: A hybrid modeling approach

The Energy Management Strategy (EMS) in an HEV is the key for improving fuel economy and simultaneously reducing pollutant emissions. This paper presents a methodology for developing hybrid models that enable EMS testing as well as the evaluation of fuel consumption, CO2 and pollutant emissions (CO, NOx and THC). In this context, pollutant emissions are hard to quantify with static models such as the well-known map-based approach which is mainly due to the pronounced impact of transient effects. The novelty of this paper primarily comes from the characterization of pollutant emissions through Convolutional Neural Networks (CNN), providing high accuracy for both, instantaneous and cumulative values. The input parameters are classical Internal Combustion Engine (ICE) measurements such as engine speed, air mass flow, torque and exhaust temperature. The proposed CNNs are reduced to a minimum for low complexity and fast computability. These models are developed with experimental data from chassis dyno testing of a conventional turbo-charged gasoline engine vehicle. The pollutant emission models are used in conjunction with physical models of the remaining powertrain allowing for real time simulations of the complete HEV vehicle. The Double Deep-Q learning algorithm is proposed for the EMS and compared to the Dynamic programming (DP) solution.The introduced methodology is developed in a co-simulation framework between MATLAB-Simulink and AMESIM. The resulting model runs between 8 and 10 times faster than real time in an off-the-shelf PC. This provides the capability for developing models suitable for HIL (hardware-in-the-loop) and SIL (software-in-the-loop) applications. The final error in predicted CO2 remains below 2.5% while the final cumulative error for pollutants is below 8.5% in the case of CO and HC emissions.

Model tests of a 10 MW semi-submersible floating wind turbine under waves and wind using hybrid method to integrate the rotor thrust and moments

Abstract. This paper describes the results of a wave tank test campaign of a 1/49 scaled SATH 10 MW floating platform. The software-in-the-loop (SiL) hybrid method was used to include the wind turbine thrust and the in-plane rotor moments. Experimental results are compared with a numerical model developed in OpenFAST of the floating wind turbine. The tank test campaign was carried out in the scaled model tested at the deep ocean basin from the Lir National Ocean Test Facility at Cork, Ireland. This floating substructure design was adapted by Saitec to support the 10 MW wind turbine within the ARCWIND project (Adaptation and Implementation of Floating Wind Energy Conversion Technology for the Atlantic Region) with the aim of withstanding the environmental conditions of the European Atlantic Area region. CENER provided the wind turbine controller specially designed for the SATH 10 MW configuration. A description of the experimental setup, force actuator configuration and the numeric aerodynamic parameters is provided in this work. The most relevant experimental results under wind and wave loading are shown in time series and frequency domain. The influence of the submerged geometry variations in the pitch natural frequency is discussed. The paper shows the simulation of a case with rated wind speed, where the tilted geometry for the computation of the hydrostatic and hydrodynamic properties of the submerged substructure is considered. This case provides a better agreement of the pitch natural frequency with the experiments than an equivalent simulation using the undisplaced geometry mesh for the computation of the hydrodynamic and hydrostatic properties.

Extension and Validation of NEMA-Style Dual-Ring Controller in SUMO

Until recently, SUMO users could not model the behavior of a ring-and-barrier traffic signal via existing signal types, which left North American SUMO users without the direct ability to capture traffic dynamics of their local networks. This work presents the methods, implementation overview, and validation of a ‘dual-ring’ NEMA style traffic controller which has recently been added to the main SUMO code base. A brief explanation of the ‘dual-ring’ implementation is also provided as context for those new to this type of traffic controller. The foundation for this work was presented at the SUMO User Conference 2021 by researchers at the US Department of Energy’s National Renewable Energy Laboratory, but was not integrated into SUMO code base at the time. Following the initial inclusion of the controller to SUMO, the authors began validation of the SUMO controller against an Econolite software-in-the-loop (SIL) traffic signal controller configured with actual setup parameters from controllers in a real-world three-intersection corridor in Tuscaloosa, Alabama, USA. This paper documents the process of adding new features to the controller code as well as validating their implementation through simulation-based and automated grey-box testing is presented in this paper. Key features such as fully-actuated operation, various timing offset plans, proper next-phase fit algorithms and more, have been added and validated against this SIL system. Though not an exhaustive demonstration of features, this work is intended make more users aware of this extension of SUMO capabilities.

Geometric Integral Attitude Control on SO(3)

This article proposes a novel integral geometric control attitude tracking scheme, utilizing a coordinate-free representation of attitude on the Lie group of rigid body rotations, SO(3). This scheme exhibits almost global asymptotic stability in tracking a reference attitude profile. The stability and robustness properties of this integral tracking control scheme are shown using Lyapunov stability analysis. A numerical simulation study, utilizing a Lie Group Variational Integrator (LGVI), verifies the stability of this tracking control scheme, as well as its robustness to a disturbance torque. In addition, a numerical comparison study shows the effectiveness of the proposed geometric integral term, when compared to other state-of-the-art attitude controllers. In addition, software-in-the-loop (SITL) simulations show the advantages of utilizing the proposed attitude controller in PX4 autopilot compared to using PX4’s original attitude controller.

A Tutorial and Review on Flight Control Co-Simulation Using Matlab/Simulink and Flight Simulators

Flight testing in a realistic three-dimensional virtual environment is increasingly being considered a safe and cost-effective way of evaluating aircraft models and their control systems. The paper starts by reviewing and comparing the most popular personal computer-based flight simulators that have been successfully interfaced to date with the MathWorks software. This co-simulation approach allows combining the strengths of Matlab toolboxes for functions including navigation, control, and sensor modeling with the advanced simulation and scene rendering capabilities of dedicated flight simulation software. This approach can then be used to validate aircraft models, control algorithms, flight handling chatacteristics, or perform model identification from flight data. There is, however, a lack of sufficiently detailed step-by-step flight co-simulation tutorials, and there have also been few attempts to evaluate more than one flight co-simulation approach at a time. We, therefore, demonstrate our own step-by-step co-simulation implementations using Simulink with three different flight simulators: Xplane, FlightGear, and Alphalink’s virtual flight test environment (VFTE). All three co-simulations employ a real-time user datagram protocol (UDP) for data communication, and each approach has advantages depending on the aircraft type. In the case of a Cessna-172 general aviation aircraft, a Simulink co-simulation with Xplane demonstrates successful virtual flight tests with accurate simultaneous tracking of altitude and speed reference changes while maintaining roll stability under arbitrary wind conditions that present challenges in the single propeller Cessna. For a medium endurance Rascal-110 unmanned aerial vehicle (UAV), Simulink is interfaced with FlightGear and with QGroundControl using the MAVlink protocol, which allows to accurately follow the lateral UAV path on a map, and this setup is used to evaluate the validity of Matlab-based six degrees of freedom UAV models. For a smaller ZOHD Nano Talon miniature aerial vehicle (MAV), Simulink is interfaced with the VFTE, which was specifically designed for this MAV, and with QGroundControl for the testing of advanced H-infinity observer-based autopilots using a software-in-the-loop (SIL) simulation to achieve robust low altitude flight under windy conditions. This is then finally extended to hardware-in-the-loop (HIL) implementation on the Nano Talon MAV using a controller area network (CAN) databus and a Pixhawk-4 mini autopilot with simulated sensor models.

Neural Network Based Model Predictive Control for a Quadrotor UAV

A dynamic model that considers both linear and complex nonlinear effects extensively benefits the model-based controller development. However, predicting a detailed aerodynamic model with good accuracy for unmanned aerial vehicles (UAVs) is challenging due to their irregular shape and low Reynolds number behavior. This work proposes an approach to model the full translational dynamics of a quadrotor UAV by a feedforward neural network, which is adopted as the prediction model in a model predictive controller (MPC) for precise position control. The raw flight data are collected by tracking various pre-designed trajectories with PX4 autopilot. The neural network model is trained to predict the linear accelerations from the flight log. The neural network-based model predictive controller is then implemented with the automatic control and dynamic optimization toolkit (ACADO) to achieve real-time online optimization. Software in the loop (SITL) simulation and indoor flight experiments are conducted to verify the controller performance. The results indicate that the proposed controller leads to a 40% reduction in the average trajectory tracking error compared to the traditional PID controller.