Hardware-in-the-loop Simulation Research Articles

Automating Fault Test Cases Generation and Execution for Automotive Safety Validation via NLP and HIL Simulation

The complexity and the criticality of automotive electronic implanted systems are steadily advancing and that is especially the case for automotive software development. ISO 26262 describes requirements for the development process to confirm the safety of such complex systems. Among these requirements, fault injection is a reliable technique to assess the effectiveness of safety mechanisms and verify the correct implementation of the safety requirements. However, the method of injecting the fault in the system under test in many cases is still manual and depends on an expert, requiring a high level of knowledge of the system. In complex systems, it consumes time, is difficult to execute, and takes effort, because the testers limit the fault injection experiments and inject the minimum number of possible test cases. Fault injection enables testers to identify and address potential issues with a system under test before they become actual problems. In the automotive industry, failures can have serious hazards. In these systems, it is essential to ensure that the system can operate safely even in the presence of faults. We propose an approach using natural language processing (NLP) technologies to automatically derive the fault test cases from the functional safety requirements (FSRs) and execute them automatically by hardware-in-the-loop (HIL) in real time according to the black-box concept and the ISO 26262 standard. The approach demonstrates effectiveness in automatically identifying fault injection locations and conditions, simplifying the testing process, and providing a scalable solution for various safety-critical systems.

Common-Mode Voltage Reduction of Modular Multilevel Converter Using Adaptive High-Frequency Injection Method for Medium-Voltage Motor Drives

This study proposes an adaptive high-frequency injection method (AHFI) aimed at mitigating common-mode voltage (CMV) on the AC side and alleviating current stress on power semiconductor devices within each arm of a medium-voltage motor propulsion system designed for modular multilevel converters. By adjusting the quantity of high-frequency components injected into each arm according to the fluctuation coefficient, the amplitude of injected high-frequency CMV and circulating currents can be reduced across medium to rated motor speeds. This approach enhances the start-up performance of medium-voltage motor drives while diminishing CMV effects on the motor side, resulting in decreased total harmonic distortion (THD) in the three-phase output waveforms. Furthermore, the effectiveness of the proposed AHFI method in SM voltage regulation and circulating current control under low-frequency operation is thoroughly analyzed. The validity of this method is established through comprehensive mathematical scrutiny and time–domain simulations performed using MATLAB/SIMULINK software, along with real-time simulations conducted employing the real-time simulator OPAL/RT via hardware-in-the-loop simulation (HILS).

Online Identification of Aerodynamic Parameters of Experimental Rockets Based on Unscented Kalman Filtering

Online identification of aerodynamic parameters of experimental rockets was completed based on unscented Kalman filtering (UKF). Numerical simulation, hardware-in-the-loop (HIL) simulation, and flight tests were conducted. The identification error of aerodynamic force in numerical simulation and HIL simulation is within 2%. For flight test data, trajectory reconstruction was performed using the identified aerodynamic forces, and the results showed that the identification results were more accurate than the interpolation table calculation results. The flight test identification results show that the identification method can complete parameter online identification under the conditions of limited performance of onboard computers, real sensor errors, and servo response. The approximate linear correlation between α and δe and the reason for their formation from the moment balance were analyzed. It was pointed out that when the recognition sampling period is long, this phenomenon will affect the identification of parameters, and a solution is proposed.

A Semi-Implicit Parallel Leapfrog Solver With Half-Step Sampling Technique for FPGA-Based Real-Time HIL Simulation of Power Converters

Field Programmable Gate Array (FPGA) based Hardware-in-the-loop (HIL) simulation is an effective tool to verify the performance of physical controllers and shorten the development cycle of power converters. In HIL simulations, sampling accuracy is of concern and is desired to be improved by reducing the step size. However, due to the cost of computational time, the step size is hard to reduce indefinitely to meet the requirements for high switching frequency applications. To improve the sampling accuracy and simulation performance of HIL simulation, this paper proposes a semi-implicit parallel leapfrog (SPL) solver with half-step sampling technique. In this solver, the switches and the rest part of the system are implemented to be computed parallel when the switch leg model operates in continuous current mode. Besides, the solver is formulated in leapfrog format to reduce computational costs and to compute at half-step as a minimum step-size. With this format, the half-step sampling technique can be employed to increase the sampling rate by onefold, even in cases where it is challenging to reduce the simulation step size further. A dual active bridge converter case is implemented on the FPGA board with a 12.5-ns sampling step-size, retaining the simulation accuracy while switched at 400 kHz. To further verify the advantages, the results are compared with other HIL method and experimental results.

Design, simulation, control of a hybrid pouring robot: enhancing automation level in the foundry industry

AbstractCurrently, workers in sand casting face harsh environments and the operation safety is poor. Existing pouring robots have insufficient stability and load-bearing capacity and cannot perform intelligent pouring according to the demand of pouring process. In this paper, a hybrid pouring robot is proposed to solve these limitations, and a vision-based hardware-in-the-loop (HIL) control technology is designed to achieve the real-time control problems of simulated pouring and pouring process. Firstly, based on the pouring mechanism and the motion demand of ladle, a hybrid pouring robot with a 2UPR-2RPU parallel mechanism as the main body is designed. And the equivalent hybrid kinematic model was established by using Eulerian method and differential motion. Subsequently, a motion control strategy based on HIL simulation technique was designed and presented. The working space of the robot was obtained through simulation experiments to meet the usage requirements. And the stability of the robot was tested through the key motion parameters of the robot joints. Based on the analysis of pouring quality and trajectory, optimal dynamic parameters for the experimental prototype are obtained through water simulation experiments, the pouring liquid height area is 35–40 cm, the average flow rate of pouring liquid is 112 cm3/s, and the ladle tilting speed is 0.0182 rad/s. Experimental results validate the reasonableness of the designed pouring robot structure. Its control system realizes the coordinated movement of each branch chain to complete the pouring tasks with different variable parameters. Consequently, the designed pouring robot will significantly enhance the automation level of the casting industry.

Representative Real-Time Dataset Generation Based on Automated Fault Injection and HIL Simulation for ML-Assisted Validation of Automotive Software Systems

Recently, a data-driven approach has been widely used at various stages of the system development lifecycle thanks to its ability to extract knowledge from historical data. However, despite its superiority over other conventional approaches, e.g., approaches that are model-based and signal-based, the availability of representative datasets poses a major challenge. Therefore, for various engineering applications, new solutions to generate representative faulty data that reflect the real world operating conditions should be explored. In this study, a novel approach based on a hardware-in-the-loop (HIL) simulation and automated real-time fault injection (FI) method is proposed to generate, analyse and collect data samples in the presence of single and concurrent faults. The generated dataset is employed for the development of machine learning (ML)-assisted test strategies during the system verification and validation phases of the V-cycle development model. The developed framework can generate not only time series data but also a textual data including fault logs in an automated manner. As a case study, a high-fidelity simulation model of a gasoline engine system with a dynamic entire vehicle model is utilised to demonstrate the capabilities and benefits of the proposed framework. The results reveal the applicability of the proposed framework in simulating and capturing the system behaviour in the presence of faults occurring within the system’s components. Furthermore, the effectiveness of the proposed framework in analysing system behaviour and acquiring data during the validation phase of real-time systems under realistic operating conditions has been demonstrated.

Initial flight test verification of software and hardware in the loop simulations of the flight stabilization system

PurposeThis paper aims to present assessment of models and simulation results used in the development process of flight stabilisation system that uses trim tabs for PZL-130 Orlik turboprop military trainer aircraft. Flight test of the system allowed to compare software and hardware simulation results with real flight recordings.Design/methodology/approachProposed flight stabilisation system was developed using modern techniques of model-based design, automatic code generation, software and hardware in the loop testing. The project reached flight testing stage which allowed to gather data to verify models and simulation results and asses their quality.FindingsResults of the comparison showed that the trim tab actuator model used in simulation can be improved by adding play. This reduced the difference between simulation and real flight system output – actuator angle. The influence of airloads on the flying actuator angle compared to hardware in the loop simulation in lab is less than ± 0.6°.Originality/valueProposed flight stabilisation system that uses trim tabs has several benefits over classic automatic flight system in terms of weight, energy consumption and structure simplicity and does not need aircraft primary control modification. It was developed using modern techniques of model-based design, automatic code generation and hardware in the loop simulations.

Hardware in the loop simulation of single-phase PWM rectifiers for improved power quality via ASWFA

Hardware in the loop simulation of single-phase PWM rectifiers for improved power quality via ASWFA

Indirect Matrix Converter Hardware-in-the-Loop Semi-Physical Simulation Based on Latency-Free Decoupling

In the process of hardware-in-the-loop simulations (HILs) of indirect matrix converters (IMCs), solving the mathematical models of complex multiswitching converter topologies has become a major problem. The conventional approach is to split the complex mathematical model into multiple serial subsystems; however, this inevitably produces delays in the simulation steps between different subsystems, leading to numerical oscillations. In this paper, the method of latency-free decoupling is adopted, which has no time-step delay between different subsystems, making each subsystem a parallel operation. This can improve the numerical stability of the simulations and can effectively reduce the step size of the real-time simulation and alleviate the problem of real-time simulation resource consumption. In this paper, we discuss in detail the modeling process of IMC hardware-in-the-loop simulations with Finite Control Set Model Predictive Control (FCS-MPC), and experimentally validate our method using the Speedgoat test platform, resulting in a simulation step size of less than 200 ns. The simulation results are compared with the results of Matlab’s Simpower power system, which allows us to evaluate the accuracy of our model.

Development of an Arduino-based, open-control interface for hardware in the loop applications

This article presents a flexible control interface based on low-cost hardware solutions for electric drives which classically come either with a proprietary hardware solution or a high-cost interface solution. The interface presented can be used to connect a standard PC with an electric drive to enable testing simulation and control applications. The control interface is developed based on the open-source Python scripting language and Arduino’s open-source and accessible hardware. The new interface communicates with the test stand through its I/O terminals via developed electronic amplifiers and creates a solid base for further development towards more extensive hardware in the loop simulations.

Enhanced Non-Communication-Based Protection Coordination and Advanced Verification Method Using Fault Impedance in Networked Distribution Systems

In recent years, the networked distribution system (NDS), which is normally connected to the distribution line (DL), was actively studied as the topology of the future distribution system for reasons such as improving supply reliability, improving line utilization, and increasing the capacity of distribution generators (DGs). However, the NDS creates new issues in terms of protection coordination because of its bidirectional power flow and fault current flow. The issues associated with conventional protection schemes in the NDS include malfunction of protective devices due to bi-directional fault currents and failure of protection coordination due to communication failures between protective devices. When applying a conventional protection method in the NDS, the protection schemes become complicated, and there is a risk of protection coordination failure due to communication failure between protective devices. To solve this problem, this paper proposes an effective and innovative non-communication-based protection algorithm for protection coordination in the NDS. The proposed protection algorithm utilizes fault impedance characteristics, which allow not only determination of whether a fault occurred, but also the ability to identify the exact fault point. Therefore, the proposed method is expected to be sustainably utilized and contribute to developing protection schemes and devices in various system topologies and scenarios in the future. Additionally, this paper addresses the overall concept of hardware-in-the-loop simulation (HILS) and directly verifies the proposed protection algorithm using HILS. Therefore, this study establishes a sustainable foundation for future research on protection coordination using HILS.

Design and Implementation of Hardware-in-the-Loop Simulation Environment Using System Identification Method for Independent Rear Wheel Steering System

In the automotive field, with the advancement of electronic and signal processing technologies, active control-based chassis systems have been developed to enhance vehicle stability. In this study, a Hardware-in-the-Loop (HiL) simulation environment was developed to effectively improve time and cost during the development process of an independent rear-wheel steering system. The HiL Simulation Environment was developed—a specific test bench capable of simulating driving loads on the prototype. Based on the system identification method, a reaction force modeling technique for the target driving loads was proposed. The full vehicle dynamics simulation model was developed with a lateral maximum error of 4.5% and a correlation coefficient of 0.98, as well as a longitudinal maximum error of 0.1% and a correlation coefficient of 0.99. The reaction force generation system had a maximum error of 2.9%. Using the developed HiL simulation environment, performance verification and analysis of the independent rear-wheel steering system were conducted, showing reductions of 5.1% in lateral acceleration and 5.2% in yaw rate.

Robust Position Control for an Electrical Automatic Transmission under Gear-Shifting Link Friction

The automotive industry is evolving, with software becoming a vital part of vehicles. Conventional automakers are shifting to software-centric entities, embracing over-the-air (OTA) updates and service-centric models. To move software-driven vehicles, the vehicle must also be electrified. Several automobile manufacturers are electrifying vehicle parts, and recently, a gear shift selector for automatic transmissions was adapted from mechanical to electronic. However, as conventional mechanical systems are modified to electrical systems, problems such as shift delay and accuracy emerge. This study addresses these problems that emerge in the electronic system type of automatic transmission, including a gear shift selector developed to electrify automobiles. Accordingly, we first analyze the structure of automatic transmission systems, then define the operation sequence. Next, a novel position control algorithm based on a disturbance observer is proposed to reduce shift delay and increase accuracy. The proposed algorithm operates harmoniously with the vehicle control unit (VCU). To verify the proposed algorithm, a hardware-in-the-loop simulation (HILS) was developed to experiment with vehicle shifting using a commercial electronic gear shift selector. Moreover, the proposed control algorithm for gear shifting in an automatic transmission was analyzed using experimental results obtained by assuming a specific driving situation in the HILS.

Hierarchical distributed MPC method for hybrid energy management: A case study of ship with variable operating conditions

The energy management (EM) strategy, power controller, and local controller of the EM system are coupled, and together affect hybrid power system performance. To achieve coordinated control and multi-objective optimisation of a hybrid power system, a hierarchical distributed control method was proposed, and a validation study was conducted based on model-in-the-loop simulations (MILs) and hardware-in-the-loop simulations (HILs). First, a real-time EM strategy based on a combination of forward dynamic programming (FDP) and model predictive control (MPC) methods was proposed to optimise the power allocation for multiple energy sources. Second, a distributed controller based on a consistency algorithm was proposed to coordinate the differences in the characteristics of multiple energy sources. A dynamic virtual impedance-based droop controller was designed for dynamic tracking of the reference power. The performance of the three-layer control structure was comprehensively validated through MILs and HILs. The results show that the dynamic virtual impedance-based droop control method can track the nonlinear reference power with control deviation within 5 %. The distributed controller is able to keep the bus voltage stable. The global control accuracy of the DC bus voltage is improved by 18.08 %, and the battery current global fluctuations is reduced by 17.4 %. The real-time FDP-MPC-based EM strategy can achieve 99.89 % energy savings and emissions reduction compared to the global DP-MPC-based method, with greater robustness, real-time performance. The results demonstrate that the proposed method is effective for real-time applications and can achieve hierarchical and multi-objective optimal control of the hybrid power system.

Distributed leader-follower based adaptive consensus control for networked microgrids

Networked microgrids (NMGs) provide a promising solution for accommodating various distributed energy resources (DERs) and enhance its performance. However, the coordinated operation of the system with integration of a large number of DERs is major challenge. Therefore, this article aims to provide a distributed control strategy (DCS) based on a leader-follower framework for the coordination of multiple DERs in NMGs. A fuzzy optimized Recurrent Hopfield Neural Network (F-HNN) designed self-adaptive fractional order proportional integral derivative (FOPID) controller is proposed for distributed frequency control of NMGs. Initially, a Lyapunov-based objective function is derived for weight updation of the proposed network. The fuzzy approach is used to optimize the output of the HNN based on its gradients. The proposed F-HNN based DCS is implemented in MATLAB/Simulink and validated for frequency regulation of NMGs through hardware-in-the-loop simulation (HIL) using OPAL-RT. The results obtained are compared with the conventional FOPID HNN tuned and other classical controls. The self-adaptiveness of the controller is demonstrated for change in renewable power generation. Furthermore, the resiliency of the controller is tested with communication failures and, plug and play operation of MGs. The results obtained showed that the frequency of the NMG system is well regulated within the band of ±0.2 Hz. Also, the transient and steady state performance reveals that the proposed DCS is more significant than other techniques.

Deep Q-Learning-Based Dynamic Management of a Robotic Cluster

The ever-increasing demands for autonomy and precision have led to the development of heavily computational multi-robot system (MRS). However, numerous missions exclude the use of robotic cloud. Another solution is to use the robotic cluster to locally distribute the computational load. This complex distribution requires adaptability to come up with a dynamic and uncertain environment. Classical approaches are too limited to solve this problem, but recent advances in reinforcement learning and deep learning offer new opportunities. In this paper we propose a new Deep Q-Network (DQN) based approaches where the MRS learns to distribute tasks directly from experience. Since the problem complexity leads to a curse of dimensionality, we use two specific methods, a new branching architecture, called Branching Dueling Q-Network (BDQ), and our own optimized multi-agent solution and we compare them with classical Market-based approaches as well as with non-distributed and purely local solutions. Our study shows the relevancy of learning-based methods for task mapping and also highlight the BDQ architecture capacity to solve high dimensional state space problems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —A lot of applications in industry like area exploration and monitoring can be efficiently delegated to a group of small-size robots or autonomous vehicles with advantages like reliability and cost in respect of single-robot solutions. But autonomy requires high and increasing compute-intensive tasks such as computer-vision. On the other hand small robots have energy constraints, limited embedded computing capacities and usually restricted and/or unreliable communications that limit the use of cloud resources. An alternative solution to cope with this problem consists in sharing the computing resources of the group of robots. Previous work was a proof of concept limited to the parallelisation of a single specific task. In this paper we formalize a general method that allows the group of robots to learn on the field how to efficiently distribute tasks in order to optimize the execution time of a mission under energy constraint. We demonstrate the relevancy of our solution over market-based and non-distributed approaches by means of intensive simulations. This successful study is a necessary first step towards distribution and parallelisation of computation tasks over a robotic cluster. The next steps, not tested yet, will address hardware in the loop simulation and finally a real-life mission with a group of robots.

Distributed collaborative optimal economic dispatch of integrated energy system based on edge computing

A distributed collaborative optimal dispatching strategy for the integrated energy system (IES), based on edge computing and consistency algorithm, is proposed in this paper. To solve the optimal dispatching problem of the IES coupled with electricity, heat, and gas, a cloud-edge-device architecture is constructed. A distributed group consensus algorithm (DGCA) is designed with two consistency protocols. The first protocol defines three groups of consistency variables, represented by the incremental cost of electricity, heat, and gas, respectively. The output estimation deviation of each component is used as a feedback. The second protocol considers the output estimation deviation of the three components as the consistency variables. The coupling variables of the two consistency protocols tend to be consistent, which can obtain the optimal output of each unit and achieve the optimal operation of the system. Furthermore, the proposed strategy accounts for transmission loss during transmission. Finally, through the hardware in the loop simulation based on the edge intelligent terminal (EIT), the effectiveness and feasibility of the strategy are verified, considering different cases.

Semi-automatic of steer by wire system using fuzzy logic control and swarm optimization

&lt;span lang="EN-US"&gt;The semi-automatic steer by wire system is a vehicle steering system that does not use a mechanical link but uses an electric drive, so that the steer wheel is equipped with an angle sensor as a control system input to adjust the electric drive rotation when determining the direction of the front wheels of the vehicle. The control system uses fuzzy logic control (FLC) is optimized using swarm-based optimization. Performance testing of the optimal control system is carried out using Software in the loop simulations and hardware in the loop simulations which were then applied to the steering system prototype equipped with rack-pinion and wheel steer. The test results show that the optimal control system performance reaches a C-RMS error of 6.712. The prototype that was built has two kinds of ratios between the steering angle and the vehicle wheel angle, namely a ratio of 1:6.25 for speeds below 10 km/h making it suitable for operation when the vehicle is maneuvering in the parking area and a ratio of 1:25 for speeds above 10 km/h which is suitable for operation when the vehicle is traveling at medium or high speed.&lt;/span&gt;

Development Steps of Avionics and Flight Control System of Flight Vehicle

The success of a research is highly dependent on the method adopted, especially research related to dangerous and expensive matters which will certainly require special handling in the development or maintenance steps. One of them is research related to space technology such as aviation and rocketry technology, which is very dependent on the design model of the flying vehicle and, in general, will always use simulation to ensure that the entire system being built is carried out safely and can be implemented properly according to plan. In the development of the prototype flying vehicle, especially the development of the avionics and flight control system, the vehicle will go through sequential simulation steps from Software in the Loop Simulation (SILS), Hardware in the Loop Simulation (HILS), and Ready-to-Fly System (RTFS). In this paper, the simulation steps will be described with the intention of facilitating integration and testing of each sub-system being developed, testing the control strategy applied or eliminating bugs if something goes wrong. In the end, with a series of flying vehicle simulations, it can be developed quickly and cost-effectively, including saving human resources.&#x0D;

Engine modelling architecture study for hybrid electric vehicle diagnosis application

Model-based control, calibration, and diagnosis are widely used in hybrid electric vehicles (HEVs) studies. For the real-time application, the contradiction lies in the model accuracy and fast response. This problem is particularly evident in engine modeling due to its complexity. In this study, three types of engine model architectures were studied for HEV Hardware In the Loop Simulation (HILS) diagnosis application. It shows that in terms of the real-time factor, the MAP model, obtaining all engine outputs only by looking up maps, has the best real-time performance. Both Mean Value Model (MVM) and Fast Running Model (FRM), with less than 3% steady-state error and preserving the engine's dynamic characteristics, have better real-time performance than the detailed model. What is more, the real-time performance of MVM is about 14% faster than that of FRM. MVM-based and FRM-based vehicle models were verified respectively in the HILS for the fault classification using support vector machine (SVM) and for the engine's misfire fault diagnosis. Since the MVM combines multiple detailed cylinders into a single mean value cylinder through neural network training, the MVM is typically used in diagnosis situations where fast computation speed is especially important and the detailed characterization of engine processes is considered relatively unimportant, such as vehicle-level fault diagnosis applications involving control system design, thermal management system fault, and power system fault, etc. Since the FRM reduces cylinder calculations by using the “cylinder translation method”, which preserves the working process within a single-cylinder cycle, it can also be used for fault diagnosis applications requiring engine performance with a crank-angle resolution, such as cylinder pressure diagnostics, combustion diagnostics, and timing control of valve and fuel injection, etc.