Hardware-in-the-loop Research Articles

Path-Following Formation of Fixed-Wing UAVs under Communication Delay: A Vector Field Approach

In many applications, such as atmospheric observation or disaster monitoring, cooperative control of a fleet of UAVs is crucial because it is effective in repeated tasks. In this work, we provide a workable and useful cooperative guiding algorithm for several fixed-wing UAVs to construct a path-following formation with communication delays. The two primary components of our concept are path-following (lateral guidance) and path formation (longitudinal guidance). The former is in charge of ensuring that, in the presence of wind disturbance, the lateral distance between the UAV and its targeted path converges using a well-known vector field technique. In the event of a communication delay, the latter ensures that several fixed-wing UAVs will create a predetermined formation shape. Furthermore, we provide a maximum delay bound that is dependent on the topology and a controller’s gain. Lastly, in order to confirm the viability and advantages of our suggested approach, we construct an effective platform for a hardware-in-the-loop (HIL) test.

Comprehensive Survey of Autonomous Driving Systems and Advanced Technologies: Literature Review, Gap Analysis and Future Directions

Abstract: This article presents a literature review of numerous research papers focusing around the topic ‘Development of Autonomous Driving Vehicle System’, and related works. The advent of autonomous driving technology has revolutionized the automotive industry, promising enhanced safety, efficiency, and convenience on the road. A critical aspect of autonomous vehicle development is the assessment of their performance ina controlled and risk-free environment. Virtual environments, enabled by cutting-edge technologies, offer a dynamic plat- form for rigorous testing and training of autonomous vehicles. This paper aims to study and analyze different approaches and methodologies that are implemented for the development of Autonomous Driving Systems(ADS). Some of the primary aspects that are found common in most papers are Virtual Environment, pedestrians’ safety, privacy, security, real-time datapresentation, precise torque control, Reinforcement Learning, Deep Learning, Hardware-in-the-loop (HIL)- simulation, Model- in-the-loop (MIL)-simulation, etc. The sole idea of extracting valuable information from a virtual environment ensures a sense of safety as there are no humans involuntarily involved to take part in the development. However, the data extracted from the virtual environment must be highly accurate and reliable, as it will be trained and tested in real environments post deployment. Collision scenarios need to be carefully studied, for which relative positioning of vehicle and pedestrians should be taken into account, so as to examine their velocities, time to collision, and appropriately taking actions. Above all, it should be noted that the safety of human lives holds the highest priority, if a method suggests a high risk factor for a human life, then it should be either discarded, or improved

An improved gain-scheduling robust MPC for path following of autonomous independent-drive electric vehicles with time-varying and uncertainties

This paper proposes a gain-scheduling robust model predictive control (GS-RMPC) algorithm for the path-following problem of autonomous independent-drive electric vehicles (AIDEVs) with consideration of time-varying and uncertainties. Firstly, the polytopic uncertainty method and norm-bounded uncertainty method are introduced to characterise the vehicle dynamics model. Secondly, the infinite predict horizon optimisation process of online GS-RMPC is transformed into a series of linear matrix inequalities (LMIs) by minimising the worst-case objective function while considering all scheduling states in the polytope. Thirdly, an offline solution is also proposed to reduce the computational burden based on asymptotically stable invariant ellipse sets. Then, a hierarchical control structure is proposed to distribute the additional yaw moment, and a multi-step predictor is designed to compensate for the actuator time delay. Finally, the hardware-in-the-loop (HIL) testing is conducted to verify the efficacy of the proposed strategy.

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.

Hybrid modelling and analysis of cutter suction dredger using hardware and software in the loop

Hybrid modelling and analysis of cutter suction dredger using hardware and software in the loop

Optimal control strategies for PMSM with a decoupling super twisting SMC and inductance estimation in the presence of saturation

This paper deals with optimal control strategies robustified by a decoupling super-twisting sliding mode control (ST-SMC) for permanent magnet synchronous machines (PMSM). In general, ST-SMC is a robust method for controlling systems and guarantees asymptotic convergence in cases where the upper bound of model uncertainties such as disturbances and parametric uncertainties is not known a priori, if the upper bound of the derivative is known. The proposed optimal control strategy is designed for a constant torque reference whose control is implemented in two control regions: maximum torque per ampere (MTPA) and flux-weakening control. In the proposed analysis, the operating point of the motor can also be in the saturation region, which makes the estimation of Ld and Lq in the proposed optimization procedure of particular interest and also important for the decoupling control implemented with ST-SMC. To compensate for typical parameter variations, adaptive parameter estimation is introduced into the control system using an extended Kalman filter (EKF) in combination with a bivariate polynomial. In order to be able to make an assessment of the control performance of the ST-SMC, a comparison with a conventional PI controller is shown at the end of the paper. Measured results using hardware in the loop (HIL) to validate the results and their detailed discussion and analysis are included.

Model Predictive Control of a Modular Multilevel Converter with Reduced Computational Burden

Recent advances in high-power applications employing voltage source converters have been primarily fuelled by the emergence of the modular multilevel converter (MMC) and its derivatives. Model predictive control (MPC) has emerged as an effective way of controlling these converters because of its high response. However, the practical implementation of MPC encounters hurdles, particularly in MMCs featuring many sub-modules per arm. This research introduces an approach termed folding model predictive control (FMPC), coupled with a pre-processing sorting algorithm, tailored for modular multilevel converters. The objective is to alleviate a significant part of the computational burden associated with the control of these converters. The FMPC framework combines multiple control objectives, encompassing AC current, DC current, circulating current, arm energy, and leg energy, within a unified cost function. Both simulation studies and real-time hardware-in-the-loop (HIL) testing are conducted to verify the efficacy of the proposed FMPC. The findings underscore the FMPC’s ability to deliver fast response and robust performance under both steady-state and dynamic operating conditions. Moreover, the FMPC adeptly mitigates circulating currents, reduces total harmonic distortion (THD%), and upholds capacitor voltage stability within acceptable thresholds, even in the presence of harmonic distortions in the AC grid. The practical applicability of MMCs, notwithstanding the presence of a large number of sub-modules (SMs) per arm, is facilitated by the significant reduction in switching states and computational overhead achieved through the FMPC approach.

An Online Energy-Saving Control Allocation Strategy Based on Self-Updating Loss Estimation for Multi-Motor Drive Systems

In this paper, an online energy-saving control allocation strategy based on self-updating loss estimation for multi-motor drive systems is proposed, where the impact of variations in motor parameters and distribution coefficients is considered. Firstly, a drive system model for multi-motor drive systems incorporating iron loss in permanent magnet synchronous motor (PMSM) is established. Then, a self-updating PMSM loss estimation method based on dynamic torque–current mapping is proposed. The torque–current mapping is initially identified based on the conv-fusion curve, and iteratively updated by optimal estimation. Subsequently, an online control allocation method based on line search is proposed, which mitigates the adverse effects caused by variations in distribution coefficients and reduces the total motor loss. Finally, the effectiveness of the proposed strategy is verified on the hardware-in-the-loop (HIL)-based platform. The results demonstrate that the strategy effectively enhances energy efficiency while maintaining the original control performance of the system.

Research on lateral stability control strategy for distributed drive electric vehicles considering driving style

To improve the handling stability of distributed drive electric vehicle (DDEV), a lateral stability control strategy considering driving style is proposed. Firstly, a driving style recognition model based on support vector machines (SVM) was constructed to identify driving styles in different lateral motion scenarios. Then, a layered architecture is used to design a driver/Active Front Steering (AFS)/Direct Yaw Control (DYC) stability coordination control strategy. The upper layer is based on the phase plane of the sideslip angle and extension control theory, dividing the vehicle operating range into classical, extension, and non domains. In the middle level, three control strategies are designed for different work domains. In the classical domain, a driver/AFS coordinated control strategy based on non-cooperative Nash games is constructed, and the weight coefficients in the AFS system cost function are modified based on driving characteristics; Construct a coordinated control strategy for AFS/DYC systems based on cooperative Pareto games in the extension domain; In non-domain, construct a direct yaw torque control strategy based on twin-delayed deep deterministic policy gradient (TD3) reinforcement learning. The lower layer aims to minimize tire utilization and optimizes the allocation of additional yaw moment. Finally, by establishing Hardware in the Loop (HIL) experiment platform, the effectiveness of the proposed control strategy is verified in lane changing scenarios of different styles of drivers. Compared to DYC stability control based solely on TD3, the maximum yaw rate and standard deviation of the lateral stability control strategy considering driving style reduce by 2.4 % and 10.82 %, respectively, and the maximum and standard deviation of the sideslip angle reduce by 29.29 % and 39.24 %, respectively, thereby improving the vehicle's handling stability.

Personalized driving behavior oriented autonomous vehicle control for typical traffic situations

autonomous driving systems not only provide services for human drivers, but also need to consider the personalized driving requirements of human beings. In current road traffic environments, the driving behaviors of drivers differ significantly. This suggests that the various needs of different drivers cannot be met by a single behavior mode in an autonomous driving decision-making system. This paper looks at the personalized characteristics of various drivers and considers the implications of their differences. First, a Proportional Integral Differential (PID) feedback channel is introduced in a traditional Model Predictive Control (MPC) to improve the performance of the controller, and comprehensively considering the collision risk, motion hysteresis and rule constraints, referring to the MPC idea, a collision avoidance method based on Q-ABSAS optimization is proposed. Then based on the Chance Constrained Programming, the control constraint is combined with driver personalization to reflect a variety of driving personality characteristics. Finally, the proposed method is tested using Hardware-in-the-loop (HIL) experiments. The experiment results demonstrate that the proposed method can successfully implement vehicle tracking control and make the vehicle's state of movement match the driver's expectations, which can increase driver comfort and driving safety.

Evaluation of start-up characteristics for heat pipe-cooled nuclear reactor coupled with recuperated open-air brayton cycle using hardware-in-the-loop

The heat pipe-cooled nuclear reactor (HPR) holds great importance for the low-carbon transition period and can be applied flexibly and stably in numerous scenarios. However, studies on the dynamic performance of the HPR coupled with the open-air Brayton cycle (OBC) are scarce, and achieving a balance between economy and accuracy has proven challenging. In this work, a hardware-in-the-loop (HIL) method is proposed and implemented for the coupled HPR-OBC prototype by combining the benefits of experimental measurements and numerical simulations. A sodium heat pipe test rig is heated in the real world, while neutronics and OBC dynamics are synchronously solved using Simulink models. A transient HIL test lasting for 12000 s is conducted to explore the start-up process of HPR-OBC. The coupled system successfully reaches steady-state by regulating reactivity and shaft speed. The results indicate a strong coupling effect between the shaft speed, the heat transfer rate of the heat pipes, and the air flow state in the OBC. The turbomachine operates within reasonable regions and no surge or choke phenomena occur. The final reached OBC efficiency is 20 %, and the generated electricity power output is 0.16 MWe.

Smart IoT Irrigation System Based on Fuzzy Logic, LoRa, and Cloud Integration

Natural resources must be administered efficiently to reduce the human footprint and ensure the sustainability of the planet. Water is one of the most essential resources in agriculture. Modern information technologies are being introduced in agriculture to improve the performance of agricultural processes while optimizing water usage. In this scenario, artificial intelligence techniques may become a very powerful tool to improve efficiency. The introduction of the edge/fog/cloud paradigms, already adopted in other domains, may help to organize the services involved in complex agricultural applications. This article proposes the combination of several modern technologies to improve the management of hydrological resources and reduce water waste. The selected technologies are (1) fuzzy logic, used for control tasks since it adapts very well to the nonlinear nature of the agricultural processes, and (2) long range (LoRa) technology, suitable for establishing large distance links among the field devices (sensors and actuators) and the process controllers, executed in a centralized way. The presented approach has been validated in the laboratory by means of a control scheme aimed at achieving an adequate moisture level in the soil. The control algorithm, based on fuzzy logic, can use the weather forecast, obtained as a cloud service, to reduce water consumption. For testing purposes, the dynamics of the water balance model of the soil were implemented as hardware in the loop, executed in a dSPACE DS1104. Experiments proved the viability of the presented approach since the continuous space state output controller achieved a water loss reduction of 23.1% over a 4-day experiment length compared to a traditional on/off controller. The introduction of cloud services for weather forecasting improved the water reduction by achieving an additional reduction of 4.07% in water usage.

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.

Two-stage advanced persistent threat (APT) attack on an IEC 61850 power grid substation

Advanced Persistent Threats (APTs) are stealthy, multi-step attacks tailored to a specific target. Often described as ’low and slow’, APTs remain undetected until the consequences of the cyber-attack become evident, usually in the form of damage to the physical world, as seen with the Stuxnet attack, or manipulation of an industrial process, as was the case in the Ukraine Power Grid attacks. Given the increasing sophistication and targeted nature of cyber-attacks, especially APTs, this paper delves into the substantial threats APTs pose to critical infrastructures, focusing on power grid substations. Through a detailed case study, we present and explore a 2-stage APT attack on an IEC 61850 power grid substation, employing a Hardware-in-the-Loop (HIL) testbed to simulate real-world conditions. More specifically, this paper discusses two significant experiments conducted to assess vulnerabilities in the control protocols used in IEC 61850 substations: IEC 60870-5-104 and IEC 61850. The integration of findings from these experiments revealed a number of previously undiscussed potential threats to power grid infrastructure that could arise from attacking one or more substations. To better address these potential threats, the paper proposes an extension to the Industrial Control System (ICS) kill chain that explicitly accounts for the consequences of attacks on the physical aspects of Cyber-Physical Systems (CPSs).

RBS and ABS Coordinated Control Strategy Based on Explicit Model Predictive Control.

During the braking process of electric vehicles, both the regenerative braking system (RBS) and anti-lock braking system (ABS) modulate the hydraulic braking force, leading to control conflict that impacts the effectiveness and real-time capability of coordinated control. Aiming to enhance the coordinated control effectiveness of RBS and ABS within the electro-hydraulic composite braking system, this paper proposes a coordinated control strategy based on explicit model predictive control (eMPC-CCS). Initially, a comprehensive braking control framework is established, combining offline adaptive control law generation, online optimized control law application, and state compensation to effectively coordinate braking force through the electro-hydraulic system. During offline processing, eMPC generates a real-time-oriented state feedback control law based on real-world micro trip segments, improving the adaptiveness of the braking strategy across different driving conditions. In the online implementation, the developed three-dimensional eMPC control laws, corresponding to current driving conditions, are invoked, thereby enhancing the potential for real-time braking strategy implementation. Moreover, the state error compensator is integrated into eMPC-CCS, yielding a state gain matrix that optimizes the vehicle braking status and ensures robustness across diverse braking conditions. Lastly, simulation evaluation and hardware-in-the-loop (HIL) testing manifest that the proposed eMPC-CCS effectively coordinates the regenerative and hydraulic braking systems, outperforming other CCSs in terms of braking energy recovery and real-time capability.

Rapid Prototyping and Hardware-In-the-Loop Verification of Enhanced Sliding Mode Control of an Asynchronous Machine Using a Xilinx System Generator and an FPGA-Zynq Board

This article describes rapid prototyping using the Xilinx system generator (XSG) of a direct field oriented control (DFOC) strategy based on improved super twisting sliding mode controllers (ISTSMCs) of an asynchronous machine to succeed a hardware implementation on a field programmable gate array (FPGA) board. The main goal is to enhance regulation loops governing rotor speed, rotor flux, and stator current components within the classical DFOC structure. These ISTSMCs replace integral proportional controllers, resulting in improved system dynamics, faster speed and torque responses, and heightened robustness against load and rotor resistance variations, surpassing other control methods. Additionally, the article aims to implement this DFOC-ISTSMC method on an FPGA board to reduce control system sampling time and loop delays, leveraging the FPGA’s parallel processing capabilities. The hardware architecture is designed using XSG in the MATLAB/Simulink environment, enabling effective simulation, testing, discrete algorithm creation, and VHDL code generation for FPGA implementation via the hardware-in-the-loop (HIL) process. Assessment involves digital simulation studies and HIL processes using XSG with a Xilinx FPGA Zynq 7000 under MATLAB/Simulink, demonstrating improved system performance, reduced time delays, and efficient FPGA utilization. This approach validates the effectiveness of the proposed DFOC-ISTSMC algorithm in enhancing control strategy for asynchronous machines.

Real-time and hardware in the loop validation of electric vehicle performance: Robust nonlinear predictive speed and currents control based on space vector modulation for PMSM

The transport industry's environmental impact has prompted various stakeholders to focus more on electric vehicles as a potential solution. Extensive research on electric vehicle motor control reveals their complexity, highlighting the crucial role of the traction motor control mechanism being one of their essential subsystems. This study presents a novel approach to control the drive system of an electric vehicle's permanent magnet synchronous motor, using a robust non-linear model predictive control in a cascaded structure combined with a space vector modulation technique. The formulation of the robust non-linear model predictive control law involves optimizing a new cost function with the inclusion of an integral action in the controller to ensure zero steady-state error. An important aspect of this control approach is its ability to enhance robustness without needing information about external perturbations and parameter uncertainties. Moreover, the space vector modulation technique is designed to address the issue of current harmonic distortion by carefully selecting appropriate switching vectors. The appropriate selection for space voltage vectors for every sampling period maintains a consistent switching frequency. As a result, the SVM can enhance torque-rippled regulation and accomplish effective flux and torque control. The performance of the proposed control system is evaluated through a series of numerical simulations using MATLAB/Simulink. The simulation results are analyzed and compared to assess the effectiveness of the proposed control system in terms of tracking accuracy, disturbance rejection, robustness against parameter uncertainties, reduction of torque, and current ripples. To further validate the numerical simulation results, a hardware-in-the-loop (HIL) setup is established using the OPAL-RT platform. Furthermore, experimental validation on a PMSM utilizing the dSPACE DS1202 board is carried out, demonstrating the efficacy of the proposed control system. These findings validate the suggested control technique's robustness and efficiency.

Dynamics Event-Triggered-Based Time-Varying Bearing Formation Control for UAVs

This article addresses the leader-follower formation maneuver control problem of multiple unmanned aerial vehicles (UAVs), taking into account the time-varying velocity and time-varying relative bearing. An event-triggered bearing-based distributed velocity observer was designed, using only the desired position and velocity of the leaders. Furthermore, a dynamic event-triggered mechanism was introduced to reduce continuous communication between UAVs, thus effectively saving communication bandwidth and resources. Building on this, a bearing-only formation maneuver control strategy was proposed, integrating the event-triggered velocity observer with the backstepping control approach. To conclude, numerical simulations have been conducted to confirm the effectiveness of the proposed scheme in accomplishing formation maneuver control objectives, including translation, scaling, and rotation control. Furthermore, the advantages of the dynamic event-triggering strategy have been demonstrated through comparative simulations with traditional event-triggering strategies. Additionally, the effectiveness of the proposed observer and controller has been demonstrated by a comprehensive hardware-in-the-loop (HITL) simulation example.

Robust Control Based on Adaptative Fuzzy Control of Double-Star Permanent Synchronous Motor Supplied by PWM Inverters for Electric Propulsion of Ships

This study presents the development of an adaptive fuzzy control strategy for double-star PMSM-PWM inverters used in ship electrical propulsion. The approach addresses the current and speed tracking challenges of double-star permanent magnet synchronous motors (DSPMSMs) in the presence of parametric uncertainties. Initially, a modeling technique employing a matrix transformation method is introduced, generating decoupled and independent star windings to eliminate inductive couplings, while maintaining model consistency and torque control. The precise DSPMSM model serves as the foundation for an unknown nonlinear backstepping controller, approximated directly using an adaptive fuzzy controller. Through the Lyapunov direct method, system stability is demonstrated. All signals in the closed-loop system are ensured to be uniformly ultimately bounded (UUB). The proposed control system aims for low tracking errors, while also mitigating the impact of parametric uncertainties. The effectiveness of the adaptive fuzzy nonlinear control system is validated through tests conducted in hardware-in-the-loop (HIL) simulations, utilizing the OPAL-RT platform, OP4510.

Asymptotic voltage restoration in islanded microgrids via an adaptive asynchronous event-triggered finite-time fault-tolerant control

As the actuators of distributed generators involved in Microgrids (MGs) may experience different faults, it is highly recommended to design an appropriate secondary Fault Tolerant (FT) controller to provide resilience and robustness with respect to these malicious events. Meanwhile, the reduction of communication network workload from controller-to-actuator may be helpful to mitigate actuators stress. By virtue of the above, this article aims at introducing a novel distributed adaptive FT-Event-Triggered Control (FT-ETC) scheme for secondary voltage regulation control in AC islanded MG able to compensate faulty actuators effects while reducing the waste of communication network resources. The proposed control scheme is derived with the aid of the backstepping method and the ETC theory, whose combination ensures resilience with respect to possible faulty signals, while no knowledge of global information is required. Adaptive mechanisms are derived by exploiting Lyapunov theory, while Zeno-freeness property is proven by contradiction. Compared with the existing state-of-the-art on FT-ETC in islanded MG, here the main novelty relies in achieving the asymptotic rather than the practical stability of the voltage tracking errors despite an unhealthy environment, while the remaining adaptive signals involved in the closed-loop systems are bounded in finite-time. Finally, an extensive simulation analysis, also involving real-time Hardware In-the-Loop (HIL) experiments, confirms the theoretical derivation.