Hardware In Loop Simulation Research Articles

Autonomous aircraft maneuvering under unknown CG variations through a robust adaptive backstepping control

The objective of the present article is to address the hitherto unaddressed problem of executing demanding autonomous aircraft maneuvers mitigating significant uncertainties in lateral center of gravity (c.g.) position. As a major contribution, it is shown that, under a reasonable simplifying assumption, a strict feedback form of the dynamics with affine in c.g. position can be derived from the highly coupled equations of motion that is caused by asymmetric c.g. variations. Thereafter, a two-step adaptive backstepping control is proposed adapting to the c.g. position automatically. Also, the useful nonlinearities in the dynamics are identified from the aircraft’s dataset and are retained to prevent a conservative control design. To provide further robustness to the aforementioned model simplification as well as to aerodynamic uncertainties, a fast adaptive sliding mode control is integrated with each of the two steps of the baseline adaptive backstepping control and the asymptotic stability of the overall system is proved using Lyapunov’s method. To validate the efficacy of the proposed control, the high angle of attack Herbst maneuver is simulated for the F18-HARV aircraft and nearly identical maneuver performance is achieved over a wide range of lateral c.g. variations on either side of the fuselage centerline. Real time hardware in loop simulations are also performed to establish the real time applicability of the proposed control.

A New Technique to Qualify Rotary Electro-Mechanical Actuator for Tactical Class of Missile System

A novel technique has been adopted to establish the functionality and to qualify the Rotary Electro-Mechanical Actuator (REMA) system developed for a typical tactical class of weapon system in addition to the conventional method of checking the functionality through On-Board Computer in-loop simulation, Actuator in-loop simulation and Hardware in-loop simulation. The REMA along with full scale fin surface subjected to wind tunnel tests at the actual flight Mach number and flight dynamic pressure conditions. Flight hardware of REMA and fin assembly has been used in the tests so as to derive direct conclusions without applying any corrections to the measured results. This technique revealed the capability of the actuator, the dynamic functionality of its components such as gear systems, bearings and structural integrity of the whole actuator under the flight aerodynamic load on the full scale fin surface and vibration environments. The test conducted is being the most critical condition and the REMA and fin combination performed the intended commands as predictable and survived without any structural dis-integrity qualifies for further flight use. Subsequently flight tests were conducted and found the performance of REMA was as anticipated. Based on these results, recommendation made that it is mandatory that the all the actuation systems developed for fin actuation in missiles has to undergo the wind tunnel tests and qualify further actual use. The details of wind tunnel tests and the results of typical REMA with full scale fin are presented in this paper.

Model compensation control of composite vertical take-off and landing UAV

Abstract The unmanned aerial vehicle (UAV) system for composite vertical take-off and landing (VTOL) is a complex, highly coupled, and nonlinear system which is sensitive to external disturbances and model uncertainties. The composite VTOL UAV system consists of a multi-rotor section and a fixed-wing section. To improve observation accuracy, the compensation function observer (CFO) uses a new structure that includes velocity information. The CFO is utilised to estimate the uncertainty and the external disturbances of the system model, which performs superior estimation accuracy compared to the extended state observer (ESO). In the modeling process of the VTOL UAV, the aerodynamic moment is calculated by means of the cross-product operation of force and force arm, which solves the problem of over-reliance on aerodynamic parameters in the traditional modeling approach. The controlled object is refined by CFO, and model compensation control (MCC) is used to realise the velocity and attitude control of the composite VTOL. The numerical simulation of MATLAB/Simulink and hardware-in-loop simulation (HIL) of Rflysim were implemented, and which were used to compare the MCC, active disturbance rejection control (ADRC), and proportion integration differentiation (PID). The simulation results confirm the superiority of MCC in controlling composite VTOL UAVs in terms of anti-disturbance and tracking speed.

Sliding Mode Controller for Temperature Control Lab Arduino Kit

Sliding Mode Control, a resilient and efficient control technique widely adopted in engineering, addresses the challenges of managing systems operating in uncertain environmental conditions. To quickly evaluate and validate the effectiveness of Sliding Mode Control, researchers can select a simple test bench like an open-source APMonitor Temperature Control Lab kit. This kit comes with MATLAB-Simulink/Python to support real-time simulation and testing. The novelty of the work is to design and implement non-linear controllers like Sliding Mode Control and Super Twisting Control techniques. Closed loop hardware in loop simulation with the designed non-linear controllers and detailed analysis is performed under tracking and disturbance rejection conditions.

Complex plane based adaptive method for protection of series compensated transmission line using phasor measurement unit data

Though differential relays are preferred for the protection of series-compensated transmission line (SCTL), they may mal-operate during current inversion, high resistance internal faults and current infeed/outfeed situations. Therefore, a new protection scheme for SCTL using Phasor Measurement Unit data, that adaptively manages the operating region in the current ratio complex plane according to the percentage compensation level, is presented. Boundaries of the operating region are determined adaptively based on the estimated impedance value of the series compensation. Usefulness of the technique is confirmed by generating cases covering wide range of fault parameters, compensation level, and load angle on an existing 400 kV Indian SCTL using PSCAD software as well as through Hardware in Loop simulation employing the Real Time Digital Simulator. Reported outcomes reveal the ability of the suggested algorithm in sensing all types of internal faults. Its performance remains unaffected during current/voltage inversion, operation of MOV and/or airgap during high fault current conditions, current infeed/outfeed situation, and external fault with current transformer saturation condition. Impact of the presence of noise in the acquired signals is also analyzed. The suggested technique is applicable for SCTLs having different voltage levels and power flow conditions. The proposed method has higher responsiveness during high resistance internal faults up to 500 Ω and better steadiness during external faults compared to numerous prevailing methods.

A Route Planning Method using Neural Network and HIL Technology Applied for Cargo Ships

This paper presents the development of a method to find optimal routes for cargo ships with three criteria: fuel consumption, safety, and required time. Unlike most previous works, operational data are used for the studies. In this study, we use data collected from a hardware-in-loop (HIL) simulator, with the plant model being a 3D dynamic model of a bulk carrier designed and programmed from 6 degrees of freedom (6-DOF) equations that can interact with forces and moments from the environmental disturbances. The dataset generated from the HIL simulator with various operating scenarios is used to train an artificial neural network (ANN) model. This predictive model then combines the A* algorithm, weather forecast data, ship parameters, and waypoint coordinates to find the optimal routes for ships before each voyage. The test results show that the proposed method works reliably, helping to improve fuel efficiency and enhance the safety of the ships.

A Route Planning Method using Neural Network and HIL Technology Applied for Cargo Ships

This paper presents the development of a method to find optimal routes for cargo ships with three criteria: fuel consumption, safety, and required time. Unlike most previous works, operational data are used for the studies. In this study, we use data collected from a hardware-in-loop (HIL) simulator, with the plant model being a 3D dynamic model of a bulk carrier designed and programmed from 6 degrees of freedom (6-DOF) equations that can interact with forces and moments from the environmental disturbances. The dataset generated from the HIL simulator with various operating scenarios is used to train an artificial neural network (ANN) model. This predictive model then combines the A* algorithm, weather forecast data, ship parameters, and waypoint coordinates to find the optimal routes for ships before each voyage. The test results show that the proposed method works reliably, helping to improve fuel efficiency and enhance the safety of the ships.

Hybrid energy storage system and its hardware‐in‐loop platform for 1500‐V metro DC power supply system based on voltage droop control

SummaryHybrid energy storage technology, which consists of lithium‐ion batteries (LiB) and super capacitors (SC), is an effective way to ensure the safety of power supply and realize energy saving in metro by reusing the braking power. Aiming at the optimal configuration and control of the metro hybrid energy storage system (HESS), an energy management strategy (EMS) based on dual DC/DC architecture and voltage droop method is proposed. And then the control parameters are adjusted in segments by combining the DC bus voltage and values of SOC of LiB and SC. What's more, a hardware‐in‐loop (HIL) simulation platform of Metro DC power supply system is built to verify the control strategy. The experimental results show that HESS could stabilize the metro voltage within a safe voltage of 580 V and achieve 100% braking energy recovery by optimal energy distribution between two different types of energy storage systems, which are only 79.9% and 39.2% in other single energy storage system by contrast. At the same time, the HIL simulation platform can be used to verify the control effect of different configuration capacities and different control strategies of the HESS, helping to provide the optimal solution of HESS for actual subway lines.

Application of machine learning controller in matrix converter based on model predictive control algorithm

Finite control set model predictive control (FCS-MPC) algorithms are famous in power converter for its easy implementation of constraints with cost function than classical control algortihms. However computation complexity increases when swicthing state is high for converters such as matrix converter, multilevel converters and this impose a serious drawback to compute multi-step prediction horizon MPC algorithm which further increases the computation. To overcome the above said difficulty, machine learning based artificial neural network (ANN) controller for matrix converter is proposed. The training data for ANN controller is derived from MPC algorithm and trained offline with an accuracy of 70.3%. The proposed ANN controller shows a similar and better performance than MPC controller in terms of total harmonic distortion (THD), peak overshoot during dynamic change in reference current and dynamic change in load parameter and less computation with less execution time. Further, ANN controller for matrix converter is tested in OPAL-RT using hardware in-loop (HIL) simulation and showed that it outperforms MPC controller.

On the performance of different Deep Reinforcement Learning based controllers for the path-following of a ship

A set of continuous state-action space-based deep reinforcement learning algorithms are used for the path following of a ship in calm water and waves. The mathematical model of a KVLCC2 tanker represents the ship dynamics. The mathematical model includes the hull force, rudder force, propulsion force, and external wave forces. Look ahead distance-based guidance algorithm called Line of Sight (LOS) is used for computing the Cross Track Error (CTE) and Heading Error (HE). The reward function is designed based on HE and CTE. The created Environment is trained with four different Deep Reinforcement Learning (DRL) agents named Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradients (DDPG), Twin-Delayed Deep Deterministic Policy Gradients (TD3), and Soft-Actor Critic (SAC). Common Neural Network architecture is used for all four agents. Yaw rate, HE, and CTE serve as input to the Neural Network, and the rudder deflection rate (δ°) corresponds to the action space (output). Computation time, average cross-track error, and rudder actuation are computed and compared for path-following scenarios. DDPG performs better with a minimum average CTE for all the simulated cases. However, SAC demands minimum rudder control effort to achieve the tasks. Finally, the trained agents are validated using Hardware In-Loop (HIL) simulation.

Online optimization of energy management strategy for FCV control parameters considering dual power source lifespan decay synergy

The decay process of fuel cell (FC) and battery is highly inconsistent. The existing research focuses on the energy management strategy (EMS) aiming at minimizing the lifespan loss of a single power source. However, this EMS cannot guarantee the optimal overall durability of dual power source systems. Based on this, this paper proposes an EMS for lifespan decay coordination of dual power sources for fuel cell vehicle (FCV). Firstly, a continuous characterization model for FC lifespan decay was developed. Secondly, the influence of control parameters such as FC response speed, filtering order, equivalent consumption minimum strategy (ECMS) equivalent factor on dual power source decay rate is analyzed. Then, the proposed energy management strategy (PEMS) is based on the cooperative control of lifespan decay of two power sources based on the condition identification. Finally, the advantages of PEMS are verified by hardware in-loop simulation. The results show that: under the working conditions of CWC1 and CWC2, compared with ECMS, PEMS reduces the difference of lifespan decay rate of dual power source by 25.62 times and 32.25 times, respectively, and the lifespan decay of fuel cell and power battery tends to be consistent. It is proved that PEMS can realize the decay cooperation of dual power source of FCV under different characteristic working conditions, and significantly improve the overall durability of dual power source system.

Design of Real Network Hardware In-Loop Simulation Test Platform for Internet of Vehicles Testing

Based on the function and performance testing requirements of Internet of Vehicles, a design scheme for automatic simulation test and verification platform for the Internet of Vehicles based on cellular networks and real network in-loop is proposed. First, the overall structure of the simulation test platform and the specific function of each component are presented. The main and functional components of the real cellular network simulation subsystem are then discussed. Finally, two typical application test scenarios are designed and presented; namely, vehicle remote-startup delay test based on a cloud platform and Internet of Vehicles local performance test of different frequency cell switching in a dynamic scenario. Experimental results verify that the simulation test platform can meet the test verification, performance evaluation, and troubleshooting requirements of the Internet of Vehicles function. It is important to improve the application experience of intelligent connected vehicles and the Internet of Vehicles.

Adaptive Control System Design and Experiment Study of Gas Flow Regulation System for Variable Flow Ducted Rockets

Variable flow ducted rockets (VFDRs) are promising candidates for propulsion systems in hypersonic vehicles because of their inherent advantages, such as high specific impulse, low weight, and high speed. The control of gas flow is essential for optimal VFDRs performance. However, the characteristics of gas flow regulation systems, such as anti-regulation, non-linearity, and parameter variation, make it difficult to construct gas flow controllers. Aiming at the above problems, we propose a compound control strategy integrating a novel second-order fuzzy adaptive tracking differentiator (SOA-TD) and an intelligent proportional-integral controller based on adaptive neuro-fuzzy inference system (ANFIS). First, a mathematical model of a gas flow regulation system was developed to analyze the control characteristics of VFDRs. Next, an ANFIS-based proportional-integral controller to developed to respond to the system’s time-varying characteristics. In addition, a novel SOA-TD was constructed to optimize the “arrange transient process” of instructions, which effectively suppressed anti-regulation of the gas flow without increasing response time. Finally, a hardware in loop (HIL) simulation device for VFDRs was established, and serial HIL simulation tests were carried out to verify the validation of the controller. The HIL simulation results indicate that our strategy exhibited a superior performance compared to traditional controllers in terms of adaptability, ability to suppress anti-regulation, and robustness, which is hoped to fulfill VFDRs’ thrust control requirements for a wide range of altitudes and Mach numbers in future engineering applications.

Performance Analysis of Renewable Integrated UPQC

The enhancement in electric power quality using a single-stage solar PV integrated Unified Power Quality Conditioner (UPQC) has been discussed in this paper. The UPQC is the combination of Distributed static compensator (DSTATCOM) and Dynamic Voltage Restorer (DVR) having the common DC voltage supply link. The DSTATCOM compensates for the load current associated problems like load power factor improvement, even and odd current harmonics elimination etc. Also, it performs the additional work of transferring power from the solar PV system to the load of the distribution system. The DVR compensates the voltage-associated power quality problems like source voltage sag, source voltage swell, and voltage distortion. Discussed UPQC with distributed generation system works on modified synchronously rotating reference frame theory. With the help of the discussed system, the two outcomes are achieved such as clean and renewable energy generation and power quality enhancement. The system is designed in MATLAB Simulink environment and then system performance is verified on Real-Time Digital Simulator (OPAL-RT OP4510) in Software in Loop Simulation (SIL) and Hardware in Loop Simulation (HIL) platforms.

Multi-Modal Traffic Simulation Calibration and Integration with Real-Time Hardware in Loop Simulator

The ongoing research in intelligent transport systems and connected and automated vehicles, enabled by advancements in artificial intelligence, integrating traffic simulations has become an essential part of product/software development for the automotive industry. Nowadays, traffic simulations are used to mimic real-world environment scenarios for virtual testing of advanced transportation technologies. With the increase in data collection methods for traffic flow, the calibration of the microscopic traffic simulations has emerged as an important research area. The underlying question in traffic modeling is how accurately simulations can mimic the real environment traffic flow conditions? This paper attempts to create a framework for microscopic traffic simulation calibration procedure which can be scaled for large networks. This paper makes the following major contributions. First, a calibration framework is proposed which harnesses the existing data set collected from The Ohio State University (OSU) campus bus service (CABS) busses using Global Positioning System (GPS) sensors to determine the traffic state in the real environment and create a microscopic traffic simulation. The traffic simulation is implemented for a section of the OSU campus (“Woody Hayes Drive") in an open-source traffic simulator – Simulation of Urban MObility (SUMO). The traffic flow generation is probabilistic to introduce variability between scenarios. The second contribution is the development of a communication interface between real-time dSpace ASM Hardware in Loop setup with SUMO to create a complete real-time simulation of urban environments for advanced driver assist systems (ADAS) virtual testing. Ademonstration scenario is the Ohio State University campus network with traffic demand generated using the calibrated model from the first part of the work.

Research on aircraft servo system modeling method in hardware in the loop simulation

The performance of the servo system has a great influence on aircraft attitude control. In application, servo system has nonlinear characteristics such as friction, clearance, zero position and so on which make it difficult to precise modeling. In engineering application, we often use the approximately linearized modeling method. However, it has its localization and cannot accurately describe the real condition of the servo devices. To test the correctness of the control algorithm in ground test, the real aircraft servo devices are often added to the whole closed-loop test system in Hardware-In-Loop-Simulation (HILS). In this paper, we introduce the traditional linearized modeling method used in HILS. To deal with the problem of nonlinear modeling, we propose a modeling method using BP neural network. Utilizing the large amount of data produced in HILS, a relatively precise model is trained. Compared with the linear modeling method, the effectiveness of neural network for nonlinear system modeling is verified. The presented method has some engineering application values.

The Experimental Evaluation of Cone Wedge Shape based Electronic Wedge Brake Mechanism in Vehicle Braking System

The brake system is one of the most critical parts of a vehicle's technology for avoiding accidents. The ultimate focus of the braking system is to guarantee that adequate stopping force is available to stop the vehicle's longitudinal movement. Therefore, the ability of a brake system to stop a vehicle must be examined in terms of analyzing the brake system's performance and the implementation of the brake system on actual vehicles. This study offers a performance evaluation of the Electronic Wedge Brake based on the Cone Wedge Shape (CW-EWB) on the vehicle brake systems. The evaluation was carried out through dynamic assessments, namely sudden braking tests at constant speeds of 40, 60, and 90 km/h using the MATLAB Simulink software simulation method and an experimental study using hardware-in-loop simulation (HILS). In the simulation study, the performance of the vehicle brake system using CW-EWB was compared with the brake performance of the vehicle using the conventional hydraulic brake (CHB). The results showed that CW-EWB behaved similarly to the hydraulic brake in terms of required brake torque output but with a faster response time, i.e., between 0.5 – 1 s. The HILS experimental study was conducted to evaluate the performance of the CW-EWB on actual vehicles. This method confirmed the HILS results against the simulation results with a variable response time of less than 6%. Vehicle body speed, wheel speed, longitudinal tire slip, and stopping distance experienced by the vehicle were all evaluated. The study's findings show that the proposed CW-EWB is quite effective and sufficiently dependable to be used as a vehicle brake system, notably in Antilock Braking Systems.

A Real-Time Digital Self Interference Cancellation Method for In-Band Full-Duplex Underwater Acoustic Communication Based on Improved VSS-LMS Algorithm

Theoretically, the spectral efficiency of in-band full-duplex underwater acoustic communications (IBFD-UWAC) is twice that of a half-duplex one. However, the actual achievable spectral efficiency of IBFD-UWAC is determined by the performance of the self-interference cancellation (SIC). In addition, the hostile underwater environment poses a challenge to the tracking performance of the SIC due to its complexity and variability. In this paper, we propose a digital SIC method based on the improved variable step-size least mean square (IVSS-LMS) algorithm where we modify the step-size adjustment criterion in the classical LMS filter and establishes a nonlinear relationship with the Sigmoid function to control the step-size using the instantaneous state error, thus improving the robustness and tracking performance of IVSS-LMS. Hardware-in-loop simulation (HLS) based on Simulink® platform verifies the real-time implementability and effectiveness of the proposed IVSS-LMS algorithm. Furthermore, the sea trial results show that the digital SIC method based on the proposed algorithm can be implemented in real-time and the convergence speed, and steady-state performance are significantly improved.

Practical Application-Oriented Energy Management for a Plug-In Hybrid Electric Bus Using a Dynamic SOC Design Zone Plan Method

The main problem in current energy management is the ability of practical application. To address the problem, this paper proposes a reinforcement learning (RL)-based energy management by combining Tubule Q-learning and Pontryagin’s Minimum Principle (PMP) algorithms for a plug-in hybrid electric bus (PHEB). The main innovation distinguished from the existing energy management strategies is that a dynamic SOC design zone plan method is proposed. It is characterized by two aspects: ① a series of fixed locations are defined in the city bus route and a linear SOC reference trajectory is re-planned at fixed locations; ② a triangle zone will be re-planned based on the linear SOC reference trajectory. Additionally, a one-dimensional state space is also designed to ensure the real-time control. The off-line trainings demonstrate that the agent of the RL-based energy management can be well trained and has good generalization performance. The results of hardware in loop simulation (HIL) demonstrate that the trained energy management has good real-time performance, and its fuel consumption can be decreased by 12.92%, compared to a rule-based control strategy.

Demonstration of Impact of STATCOM on Loss of Excitation Protection Through Real Time Hardware in-Loop Simulation

Demonstration of Impact of STATCOM on Loss of Excitation Protection Through Real Time Hardware in-Loop Simulation