Hardware-in-the-loop Research Articles

Hardware-in-the-loop simulation to validate the fractional-order neuro-fuzzy power control of variable-speed dual-rotor wind turbine systems

New and efficient command strategies and algorithms are needed in the field of wind turbine energy generation to ensure power quality according to international standards. However, conventional linear strategies are still used to command doubly-fed induction generators (DFIGs). Thus, inadequate power quality is obtained, which can cause network-level disturbances. This Hardware-in-the loop (HIL) study presents a new command scheme for a DFIG-based dual-rotor wind turbine (DRWT) system. The new command is a combination of a neuro-fuzzy algorithm and fractional-order control that overcomes the declining power quality of the DFIG-DRWT system. The new strategy is based on using pulse width modulation to generate command pulses for the rotor-side converter of DFIG. In the designed command system, the reference value of the active power is determined using maximum power point tracking to provide efficient power conversion performance under different operating conditions. First, MATLAB software is used to implement the fractional-order neuro-fuzzy (FONF) control technique, and the behavior of the proposed strategy is studied in comparison to that of the traditional direct power command (DPC) technique. A comparison is performed in terms of the ripple minimization ratio, durability, overshoot, steady-state error, reference tracking, and current quality. The results of the comparison show the high performance of the FONF technique. Second, the FONF technique is implemented in HIL test using the dSPACE 1104 Card, where two different forms of wind speed are used to study the characteristics of the proposed strategy and confirm its effectiveness and performance in HIL test compared to DPC technique. The experimental results obtained in the two tests confirm the efficiency, effectiveness, and high performance of the proposed FONF technique compared to DPC technique in improving the energy system characteristics, and the simulation obtains the same results.

A versatilely high fidelity electric machines emulator for rapid testing of motor controller.

Electric machines emulators (EMEs) based on hardware-in-the-loop (HIL), which effectively act as emulators to mimic the actual motor behavior of Interior Permanent Magnet (IPM) machines. EME is frequently used to evaluate motor controller and motor control methodologies prior to development. The inverse magnetization motor model, which is used as the basis for real-time simulation in this paper's proposal for an electric machine emulator system based on HIL, uses FEA to create the motor model data. The nonlinear features of the motor may be successfully replicated with this motor model, and the accuracy of the electric machine emulator can be enhanced by using a straightforward and trustworthy motor controller. The real-time simulation tool typhoon HIL is used in the study to develop a hardware-in-the-loop simulation platform for an IPM electric machines emulator.

Magnetic integrated double-trap filter utilizing the mutual inductance for reducing current harmonics in high-speed railway traction inverters

Current harmonics are generated at the switching frequency and its multiples when the traction converters are modulated. To address this, multi-trap filters are introduced, which are capable of selectively eliminating these specific harmonics to the limits set by IEEE 519-2014. This targeted removal significantly reduces the need for high total inductance, thereby allowing for a more compact filter design. Comparatively, to traditional inductor-capacitor-inductor (LCL) filters, more magnetic cores are needed for trap inductors. Furthermore, the traction systems have not been examined in conjunction with multi-trap filters. To reduce the filter size and investigate its application in traction converters, this paper presents an integrated double-trap LCL (DTLCL) filter. A tiny capacitor is connected in parallel with the grid-side inductor to form one LC-trap. In addition, another LC-trap is formed by connecting the equivalent trap inductor, introduced through the magnetic coupling between inverter-side and grid-side inductors, in series with the filter capacitor. The presented filters' features are thoroughly analyzed, and the design method has been developed. Finally, the simulation and hardware-in-the-loop (HIL) experiment results validate the proposed method's viability and efficacy. Compared to the discrete windings, the integrated ones enable a size decrease of two cores. Furthermore, the proposed filters can meet IEEE 519-2014 criteria with 0.3% for all the current switching harmonics and total harmonic distortion (THD) of 2.36% of the grid‐side current.

A Study on Power Transmission Control for Applying MR Fluid Multi-Plate Clutch to Automobile Power Distribution Device

The aim of this study is to design and manufacture a multi-plate clutch system that uses magnetorheological (MR) fluid control to allow for a variable power transmission ratio in power distribution systems. MR fluid is a smart material that enables presenting a solution to the shocks and power loss that occur due to mechanical problems in power distribution systems. As such, the longitudinal and lateral dynamic properties of 4WD (four-wheel drive) vehicles were examined and analyzed to develop an algorithm to control the front/rear power distribution according to the road surface state and driving conditions. To verify the algorithm, the CarSim vehicle dynamics simulation program was adopted to perform experiments to understand the vehicle’s dynamic performance improvements and turning stability via a HILS (Hardware in the Loop) system. In this study, an MR fluid, multi-plate clutch was used that combines a dry clutch and a wet clutch using the characteristics of the MR fluid. Such a clutch was designed to enable continuous and smooth torque transmission by utilizing the strengths of each of the dry and wet clutches. The CarSim vehicle dynamics program was used to conduct the experiments, which were conducted by linking to the manufactured MR fluid clutch experimental device. The experiments investigated the dynamic performance based on the power distribution ratio by performing longitudinal flat, inclined driving and lateral DLC (double lane change) driving. In summary, this study found that it is possible to perform power transmission by applying a current to an MR fluid and forming a magnetic field to change the flow properties of the fluid to control the torque transmission ratio that occurs in an MR fluid clutch.

Hardware-in-the-loop dynamic load emulation of robotic systems actuated by fluidic artificial muscles

Hardware-in-the-loop (HIL) testing is a popular control system testing method because it bridges the gap between modeling/simulation and experiments. Instead of designing a full hardware-based experiment to validate the results of a simulation, the plant hardware can be replaced with an emulator device that responds to exogenous inputs and effectively emulates the dynamic behavior of a system. This approach can be more cost-effective and modular, since the emulated plant system can be modeled in a simulation environment, implemented on a simplified piece of hardware and changed quickly without having to fabricate new parts. This paper develops the hardware and control scheme for a certain type of HIL device called a dynamic load emulator that consists of a 1-DOF linear hydraulic dynamometer equipped with in-line sensing to measure both its own position and the force exerted on it by a device-under-test. This measured force is passed to a real-time model of the emulated dynamic system. The model outputs the emulated system position, and a closed-loop controller is used to emulate this position. The emulator controller incorporates both model-based feedforward and standard feedback PI control. This paper characterizes the dynamometer-based dynamic load emulator and its controller, determining its hardware limitations and validating its capabilities when experiencing a force input from a linear spring with known parameters. Additionally, this paper demonstrates the ability of the emulator to represent the dynamics of a 1-DOF robotic joint when actuated by a pair of fluidic artificial muscles (FAMs). The primary contribution of this work is to allow for more comprehensive testing of FAM configurations, topologies, and controllers for a wide range of parameters, because the same hardware can be used to emulate multiple systems. As a result, this work will lead to more cost-effective, time-efficient, and energy-efficient designs of robotic systems and the FAMs used to actuate them.

Finite‐time sliding mode fault‐tolerant control of PEM fuel cell air supply system

AbstractProton exchange membrane (PEM) fuel cell air supply system is primarily responsible for delivering air to the fuel cell at a specific stoichiometric ratio for chemical reaction. The stoichiometric ratio will be impacted by the sudden change in load and system failure. Under the aforementioned circumstances, precise and timely air supply regulation is required to avoid irreparable PEM damage. In order to enhance the robustness and performance of the fuel cell, a cascaded finite time sliding mode fault‐tolerant control method is proposed for the air supply system of PEMFC. The control objective is to adjust the oxygen excess ratio at the cathode flow field, which can avoid the occurrence of oxygen starvation in the PEM fuel cell when the load current changes rapidly or when fault occurs. A cascaded controller structure is introduced, where, in the inner loop, an adaptive sliding mode controller is designed to reconstruct fault signal and track the optimal output trajectory and, in the outer loop, a finite‐time observer is employed to obtain the expected output trajectory. The proposed control scheme is capable of fault‐tolerant tracking of the optimal oxygen excess ratio within finite time. The effectiveness of proposed fault‐tolerant control algorithm is verified through hardware‐in‐the‐loop (HIL) studies.

Hybrid Power System Design and Dynamic Modeling for Enhanced Reliability in Remote Natural Gas Pipeline Control Stations

The most rapid and efficient method to transport natural gas from its source to its destination is through a pipeline network. The optimal functioning of control stations for natural gas pipelines depends on the use of electrical devices, including data loggers, communication devices, control systems, surveillance equipment, and more. Ensuring a reliable and consistent power supply proves to be challenging due to the remote locations of these control stations. This research article presents a case study detailing the design and dynamic modeling of a hybrid power system (HPS) to address the specific energy needs of a particular natural gas pipeline control station. The HOMER Pro 3.17.1 software is used to design an optimal HPS for the specified location. The designed system combines a photovoltaic (PV) system with natural gas generators as a backup to ensure a reliable and consistent power supply for the control station. Furthermore, it provides significant cost savings, reducing the cost of energy (COE) by USD 0.148 and the annual operating costs by USD 87,321, all while integrating a renewable energy fraction of 79.2%. Dynamic modeling of the designed system is performed in MATLAB/Simulink R2022a to analyze the system’s response, including its power quality, harmonics, voltage transients, load impact, etc. The experimental results are validated using hardware in the loop (HIL) and OPAL-RT Technologies’ real-time OP5707XG simulator.

A hardware-in-the-loop (HIL) testbed for cyber-physical energy systems in smart commercial buildings

In recent years, there has been a growing trend toward the development of smart buildings that rely on cyber-physical systems (CPS) to optimize occupant comfort, safety, and energy efficiency. To ensure the reliable and efficient operation of CPS with designed control strategies, it is important to evaluate their performance under various scenarios before deploying them in the real world. This is where a Hardware-in-the-loop (HIL) testbed designed for studying sensor and control-related studies in smart buildings can be highly valuable. With the growing threat of cyber-attacks and physical faults targeting smart buildings, it is essential to ensure the security of building operations. A HIL testbed can emulate cyber-attack and physical fault scenarios, allowing researchers to develop and test threat detection and mitigation algorithms. This enables researchers to identify potential issues and optimize the algorithms in a safe and controlled environment before they are deployed in real-world settings, reducing the risk of failures that can negatively impact occupant comfort, safety, and energy efficiency. Therefore, this paper developed a HIL testbed designed for cyber-physical energy systems (e.g. buildings automation system (BAS)) in smart commercial buildings. The HIL testbed is comprised of a real-time building and Heating, Ventilation, and Air-Conditioning (HVAC) emulator using Modelica-based dynamic models, a set of BAS controllers, and a BAS computer server. The data generation capability of the HIL testbed is demonstrated by tracking normal and faulty operating data in the BAS, as well as monitoring detailed network traffic in the local BAS network. This study further demonstrates the HIL testbed’s capability by conducting case studies on real-time physical fault and cyber-attack experiments using a Department of Energy (DOE) prototype commercial building. It is anticipated that the fully functional HIL testbed will be utilized for a variety of sensor and control-related studies, including but not limited to testing, developing, validating of different HVAC control strategies, fault detection & diagnosis, energy monitoring and analysis, cyber security study, etc.

Online Predictive Visual Servo Control for Constrained Target Tracking of Fixed-Wing Unmanned Aerial Vehicles

This paper proposes an online predictive control method for fixed-wing unmanned aerial vehicles (UAVs) with a pan-tilt camera in target tracking. It aims to achieve long-term tracking while concurrently maintaining the target near the image center. Particularly, this work takes the UAV and pan-tilt camera as an overall system and deals with the target tracking problem via joint optimization, so that the tracking ability of the UAV can be improved. The image captured by the pan-tilt camera is the unique input associated with the target, and model predictive control (MPC) is used to solve the optimization problem with constraints that cannot be performed by the classic image-based visual servoing (IBVS). In addition to the dynamic constraint of the UAV, the perception constraint of the camera is also taken into consideration, which is described by the maximum distance between the target and the camera. The accurate detection of the target depends on the amount of its feature information contained in the image, which is highly related to the relative distance between the target and the camera. Moreover, considering the real-time requirements of practical applications, an MPC strategy based on soft constraints and a warm start is presented. Furthermore, a switching-based approach is proposed to return the target back to the perception range quickly once it exceeds the range, and the exponential asymptotic stability of the switched controller is proven as well. Both numerical and hardware-in-the-loop (HITL) simulations are conducted to verify the effectiveness and superiority of the proposed method compared with the existing method.

Hardware-in-the-loop setup for enhanced modular multi-level converter with reduced circulating currents

Owing to its essential features, such as modularity and exceptional power quality, the modular multilevel converter (MMC) emerges as the optimal converter topology for high-voltage direct current (HVDC) applications. Traditionally, MMCs are controlled through a method called nearest level modulation (NLM), which generates N+1 AC output voltages, where N represents the number of sub modules (SMs) per arm. In this paper, we introduce a modified NLM technique designed to yield 2N+1 and 4N+1 levels, with a focus on efficiently controlling internal dynamics. The proposed MMC is evaluated using a hardware-in-the-loop (HIL) environment to obtain real-time simulation outcomes. This MMC topology demonstrates a reduction in circulating currents and capacitor voltage ripple.

Privacy-Preserving Robust Consensus for Distributed Microgrid Control Applications

Consensus-based distributed control has been proposed for coordinating distributed energy resources (DERs) in microgrids (MGs). As one key component, distributed average observers are used to estimate the average of a group of reference signals (e.g., voltage, current, or power). State-of-the-art distributed average observers could lead to loss of privacy due to information exchange on the communication channels. The DERs' reference signals, which contains private information, could be inferred by an eavesdropper. In this paper, a privacy-preserving distributed average observer is proposed, which is based on robust consensus and uses the state decomposition method to preserve privacy. Compared to the existing methods, the proposed observer does not require the knowledge of the reference signal's derivative and gives accurate and smooth estimation, and is thus applicable for MG distributed control applications. A detailed analysis regarding the convergence and privacy properties of the proposed observer is presented. The proposed observer is implemented on hardware controllers and validated in the context of distributed MG control applications through hardware-in-the-loop (HIL) tests.

Dynamic Event-Triggered Sensor Fault Interval Estimation and Accommodation for Aeroengine

This paper concerns with the dynamic event-triggered fault interval estimation and accommodation for aeroengine sensors. Firstly, a dynamic event-triggered based robust augmented state observer is designed to reconstruct sensor fault for an aeroengine subject to external disturbance and measurement noise. Next, the event-triggered parameters and fault-tolerant controller (FTC) are co-designed based on the obtained observer gains to save communication resources. Moreover, the interval estimation of sensor fault is achieved via reachability analysis. Eventually, the designed scheme is tested with aeroengine Hardware-in-the-loop (HIL) experiment, which illustrates its potential in aeronautical engineering.

A high-order disturbance nutation/wobble control for unbalanced spinning satellites

This paper introduces a novel active nutation/wobble rejection control method for spinning satellites enduring thrusting disturbances and reaction wheel limitations. Our approach employs two tilted reaction wheels in reaction-momentum mode, actively controlling wobble/nutation while enhancing disturbance rejection for dynamically unbalanced satellites. This eliminates the need for balancing processes or additional ballast, reducing system weight. A new wheel arrangement and momentum analysis effectively dampens wobble generated by dynamic imbalances. We prove that increasing wheel angular speed about the spinning axis enhances immunity against transverse disturbances. Unlike existing controllers, our method minimizes average thrust/spin axis oscillation while eliminating undesired rates and wobble. Stability is rigorously established through linearized equations and Routh stability criteria. Validation via air-bearing hardware-in-the-loop (HIL) and Monte Carlo tests demonstrates close agreement between predictions and experimental data, highlighting the controller's robustness against high thrusting disturbances. This work represents a significant contribution by achieving active stabilization of unbalanced spin satellites using onboard reaction wheels, potentially paving the way for wider adoption in future spacecraft designs.

Precision Advancements in Aerial Gliding Vehicles: Modeling to FCS Validation

The growing utilization of unmanned aerial vehicles (UAVs) in military operations has necessitated the development of a suitable weaponry for these kind of platforms. One of the trending categories of such armaments is the aerial gliding vehicle (AGV). AGVs have no propulsion system, consequently, a critical need for a robust flight control system (FCS) tailored to this kind of aerial systems is raised. This research focuses on designing a nonlinear model based controller, starting with the construction of a precise model through practical experiments and the establishment of a dedicated testing and flight simulation environment. Recognizing the limitations of traditional nonlinear dynamic inversion (NDI) due to its dependence on the vehicle model, the modified incremental nonlinear dynamic inversion (MI-NDI) is developed to operate in the presence of wind, model mismatches, and external disturbances. In this research, an extensive testing is conducted in a hardware-in-the-loop (HIL) simulation environment which validates the MI-NDI controller’s superior performance, even in challenging conditions. The research outcomes mark a significant advancement in enhancing autopilot precision for advanced aerial weaponry and unmanned vehicles.

A Cyber-Physical Testbed for IoT Microgrid Design and Validation

Microgrids are small power systems, often equipped with renewable energy sources, that are alternatives or supplementary to utility grids. Many studies have been conducted on the design and implementation of microgrids and their interconnects to utility grids, and investigations have been extended to the use of Internet of Things technology (IoT) to monitor and operate such power grids. However, the broad applications of the IoT technology itself also call for a green energy solution. This paper investigates how to power local IoT applications via an integration of a microgrid and the utility grid. Together, we call such a system an IoT microgrid. The goal of an IoT microgrid is to maintain the availability of IoT applications while saving energy costs, and this is achieved by sustaining IoT applications via local renewable energy from a microgrid and by mitigating the intermittent power supply using the utility grid. This paper characterizes the IoT microgrid and proposes a configurable cyber-physical testbed for its design and validation. The testbed incorporates the hardware-in-the-loop (HIL) approach, where real-time simulation is integrated with physical elements for quick prototyping of those components in an IoT microgrid. The paper concludes with an example implementation of the proposed testbed, which demonstrates its use for validating both an IoT microgrid and the IoT application it sustains.

Networked explicit hybrid predictive control of an industrial benchmark process

Hybrid systems have acquired deep attention in research as many systems in industries and laboratories manifest hybrid dynamic behaviors. Optimal control design for hybrid systems exhibits challenges for controlling the interacting continuous and discrete dynamics. A double-tank hybrid system (DTHS) is proposed in this paper with a mixed-integer actuation mechanism. The proposed DTHS aims to control the liquid level of the two tanks simultaneously by a networked hybrid predictive controller. Hybrid frameworks are provided to investigate the system through reachability analysis. An explicit hybrid model predictive control (EHMPC) has been developed to control the levels of the DTHS. Hardware-In-the-Loop (HIL) simulation is performed for assessing the performance of the EHMPC with a hardware real-time networked controller. The networked real-time controller is constructed and implemented using the Node.js frameworks. The simulation and realtime results are presented and discussed to evaluate the hybrid controller performance.

Integrated Path Following and Lateral Stability Control of Distributed Drive Autonomous Unmanned Vehicle

Intelligentization is the development trend of the future automobile industry. Intelligentization requires that the dynamic control of the vehicle can complete the trajectory tracking according to the trajectory output of the decision planning the driving state of the vehicle and ensure the driving safety and stability of the vehicle. However, trajectory limit planning and harsh road conditions caused by emergencies will increase the difficulty of trajectory tracking and stability control of unmanned vehicles. In view of the above problems, this paper studies the trajectory tracking and stability control of distributed drive unmanned vehicles. This paper applies a hierarchical control framework. Firstly, in the upper controller, an adaptive prediction time linear quadratic regulator (APT LQR) path following algorithm is proposed to acquire the desired front-wheel-steering angle considering the dynamic stability performance of the tires. The lateral stability of the DDAUV is determined based on the phase plane, and the sliding surface, in the improved sliding mode control (SMC), is further dynamically adjusted to obtain the desired additional yaw moment for coordinating the path following and lateral stability. Then, in the lower controller, considering the slip and the working load of four tires, a comprehensive cost function is established to reasonably distribute the driving torque of four in-wheel motors (IWMs) for producing the desired additional yaw moment. Finally, the proposed control algorithm is verified by the hardware-in-the-loop (HIL) experiment platform. The results show the path following and lateral stability can be coordinated effectively under different driving conditions.

Predictive energy management of fuel cell plug-in hybrid electric vehicles: A co-state boundaries-oriented PMP optimization approach

This paper proposes a predictive energy management strategy of FC PHEV based on PMP and co-state boundaries. The model predictive control (MPC) problem is established and transformed as a two-point-boundary-value one by PMP theory, and the physical constraints of FC power, FC power varying rate, and battery current, are merged by methodical derivatives. To gain the accurate co-state boundaries, the Karush-Kuhn-Tucker condition, for the first time, is employed to derive the general expressions, and a correction method is developed to modify the co-state boundaries for effectiveness guarantees. By inputting the feedback SOC, power demand, and the unified constraint, the shrunken enclosed range between the developed co-state boundaries can be determined in real time, thereby benefiting the efficient co-state calibration. Based on the co-state bounds, the concise but highly effective heuristic rules are proposed to calibrate the co-state online iteratively, and an analytical method is proposed to fast find the optimal solution of Hamilton function. The global optimality of the proposed strategy for the addressed MPC problem is also strictly proved. The validations and sensitivity analysis for the initial state of charge (SOC), the SOC reference, the predictive velocity accuracy, and the horizon length, are implemented under simulations, and the hardware-in-the-loop (HIL) experiments are carried out to verify the effectiveness of the proposed strategy under on-board environment. The results yield that, the proposed strategy can implement the timely and high-efficiency co-state updates, the smooth control commands, the expected SOC tracking effects, and improve the fuel economy. Additionally, <0.5 ms per sample is spent for the predictive horizon length 40 under HIL tests, indicating the real-time applicability of proposed strategy.

Adaptation of Microinverter Reference Design for Integration with Battery Energy Storage Systems in Microgrids

The paper presents an adaptation of the microinverter platform from Texas Instruments to incorporate a battery energy storage system (BESS) alongside the development of the BESS system itself. Initially designed for unidirectional power flow between PV panels and an electric grid, the platform required modifications to accommodate bidirectional energy transfer for BESS integration. These modifications encompass software adjustments and hardware enhancements, which are all detailed within the paper. The electrical configuration includes selecting and deploying components such as DCDC power converters, microcontrollers, measured signals, and actuating signals to facilitate battery connection to the platform’s DC bus. Furthermore, a supervisory control and data acquisition (SCADA) system is devised for supervisory control and monitoring, with its implementation outlined. Control software tailored for the chosen microcontroller of the DCDC converters is described in terms of structure and functionality. A hardware-in-the-loop (HIL) methodology is employed to validate the proposed modifications and microgrid configuration. Utilizing the real-time simulator OPAL-RT, the paper presents experimental results and their analysis within the considered microgrid environment.

Four-period integration oversampling method (4PIOM) for hardware-in-the-loop power converters with complementary switches

This paper addresses aliasing oscillations encountered in hardware-in-the-loop (HIL) simulation caused by inaccurate duty cycle detection in high-frequency power electronic applications. Oversampling has been commonly used as a solution to detect switching events more accurately. Traditional oversampling methods use the extra information obtained by oversampling the inputs for further computations to enhance the precision of the simulation. However, these techniques increase the complexity of the model since they take into account several switch states during each simulation step. To mitigate these complexities, the integration oversampling method (IOM) was introduced as a recent alternative with minimum impact on the model complexity. IOM provides a modified switching pattern that effectively prevents aliasing oscillations while maintaining a single switch-state for each simulation step. It can be implemented as an independent block between the controller and the HIL model, so it keeps the HIL model unchanged. This study highlights the limitations detected in applying IOM to the models with complementary switches, including possible undesired short circuits. To overcome these limitations, a novel oversampling method called 4PIOM is presented. 4PIOM further enhances the IOM algorithm by reducing the simulation step and sampling period. The validity of the new method is demonstrated by comparison with previous proposals and also with the same model without any oversampling. Both experimental and MATLAB simulation results prove its superior performance in attenuating the aliasing oscillations and improving the quality of the simulation.