Model-in-the-loop Research Articles

Human sleeping pose estimation from IR images for in-bed patient monitoring using image processing and deep learning techniques

Human Pose Estimation (HPE) is a crucial step towards understanding people in images and videos. HPE provides geometric and motion information of the human body, which has been applied to a wide range of applications (e.g., human-computer interaction, motion analysis, augmented reality, virtual reality, healthcare, etc.). An extremely useful task of this kind is the 2D pose estimation of bedridden patients from infrared (IR) images. Here, the IR imaging modality is preferred due to privacy concerns and the need for monitoring both uncovered and covered patients at different levels of illumination. The major drawback of this research problem is the unavailability of covered examples, which are very costly to collect and time-consuming to label. In this work, a deep learning-based framework was developed for human sleeping pose estimation on covered images using only the uncovered training images. In the training scheme, two different image augmentation techniques, a statistical approach as well as a GAN-based approach, were explored for domain adaptation, where the statistical approach performed better. The accuracy of the model trained on the statistically augmented dataset was improved by 124 % as compared with the model trained on non-augmented images. To handle the scarcity of training infrared images, a transfer learning strategy was used by pre-training the model on an RGB pose estimation dataset, resulting in a further increment in accuracy of 4 %. Semi-supervised learning techniques, with a novel pose discriminator model in the loop, were adopted to utilize the unannotated training data, resulting in a further 3 % increase in accuracy. Thus, significant improvement has been shown in the case of 2D pose estimation from infrared images, with a comparatively small amount of annotated data and a large amount of unannotated data by using the proposed training pipeline powered by heavy augmentation.

Exploring the Executable SysML Capabilities to Integrate and Operate Hardware in the Loop

AbstractThese days, systems engineers use advanced software tools and standardized modeling methods to break down complex systems into simpler, less complicated parts that are easier to understand. This includes CATIA Magic that allows for systems architecture, as well as more in‐depth Systems Modeling Language (SysML) modeling and simulation. The goal of this paper is to explore the capabilities of SysML to model and control hardware in the loop (HIL). The model of a weapon system was created using several different diagrams commonly used in SysML. When these diagrams are used together with the CATIA Magic simulation toolkit (Magic Model Analyzer), they can be made executable and used for simulation purposes. While CATIA Magic was selected to provide a digital model of the weapon system, a LEGO Mindstorms EV3 development kit was selected to create a mockup of a physical system. The CATIA Magic model reacts to the inputs received from the LEGO sensors and sends control signals to motors built into the LEGO model. In general, the integration between the LEGO Mindstorms and CATIA Magic can be done successfully, however, this study revealed certain limitations pertinent to the current capability of CATIA Magic to make connections between the SysML model and LEGO hardware. While these limitations did not allow for a full integration and in‐depth testing of the created dynamic model, as intended, the paper presents the current state of the project discussing what is involved in integrating CATIA Magic and LEGO Mindstorms kits and what functionality could be achieved by doing this. The paper pursues both the educational aspect that allows a deeper exploration of the modern system modeling tools, and the practical aspect, showing how a SysML model can be integrated with HIL to provide opportunities to better understand the design space at earlier stages of system development.

Optimization and Analysis of Clutch Switching Timing for Hybrid Tractors Equipped with Hydraulic Mechanical Combined Transmission

The working environment of tractors is harsh and the working conditions are very complicated. To cope with this complex and changeable working environment, this paper presents a hybrid tractor powertrain loaded with hydraulic mechanical combined transmission (HMCVT). The powertrain can switch modes according to different operating environments. During mode switching, impact loads can cause wear and even damage to the transmission components. Interrupting the power transmission will reduce the tractor’s operational efficiency. In this paper, the orthogonal experimental range analysis method was proposed to optimize the quality of mode switching. Mode switching involves interactions between clutches. Orthogonal table L16 (215) was selected to design the orthogonal table head. SimulationX was used for simulation. Employing MINITAB range analysis yielded the optimal results. The simulations indicated a reduction in the output shaft’s velocity drop amplitude from 17.647% to 6.591%. The dynamic load coefficient decreased from 2.743 to 1.857. The impact strength was reduced from 14.125 to 5.67 m/s3. The switching time was reduced from 2.13 to 1.71 s. We built a model-in-the-loop (MIL) test platform to validate the results. The MIL test results were compared with the simulation results. Our findings indicate a degree of discrepancy between the simulated and the experimental results, yet the overall trends remain largely consistent. The correctness of the optimal scheme obtained from the orthogonal experiment is verified. This study provides a theoretical basis for the optimization of powertrain mode switching in hybrid tractors equipped with HMCVT.

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

Towards robust state estimation for LFP batteries: Model-in-the-loop analysis with hysteresis modelling and perspectives for other chemistries

The accurate estimation of a battery’s state of charge (SOC) is critical in battery management systems for various applications. Lithium Iron Phosphate (LFP) batteries, preferred for their long cycle life, cost efficiency, and enhanced safety, have emerged as favourable choices for stationary storage. Yet, they still face challenges in precise SOC estimation due to the flatness and hysteresis of their open circuit voltage. Addressing this, our study integrates a hysteresis model into a third-order battery model for BMS controlling a stationary storage system in frequency containment reserve (FCR) application. We analysed three advanced SOC estimation techniques — extended Kalman filter (EKF), dual unscented Kalman filter (DUKF), and particle filter (PF) — with the hysteresis model using a model-in-the-loop (MiL) toolchain. Performance testing under a 48-hour FCR load profile showed EKF with a 4% error, DUKF achieving the best result with a 1.1% error, and PF’s performance varying between 2.9% and 4% depending on particle count. Robustness tests against initialization and current sensor errors under an 8hr profile revealed DUKF maintained a 2% error boundary irrespective of the error introduced, highlighting the hysteresis model’s effectiveness. Broadening the scope, the study also explores extending the method to other lithium-ion chemistries.

Deep-Reinforcement-Learning-Based Collision Avoidance of Autonomous Driving System for Vulnerable Road User Safety

The application of autonomous driving system (ADS) technology can significantly reduce potential accidents involving vulnerable road users (VRUs) due to driver error. This paper proposes a novel hierarchical deep reinforcement learning (DRL) framework for high-performance collision avoidance, which enables the automated driving agent to perform collision avoidance maneuvers while maintaining appropriate speeds and acceptable social distancing. The novelty of the DRL method proposed here is its ability to accommodate dynamic obstacle avoidance, which is necessary as pedestrians are moving dynamically in their interactions with nearby ADSs. This is an improvement over existing DRL frameworks that have only been developed and demonstrated for stationary obstacle avoidance problems. The hybrid A* path searching algorithm is first applied to calculate a pre-defined path marked by waypoints, and a low-level path-following controller is used under cases where no VRUs are detected. Upon detection of any VRUs, however, a high-level DRL collision avoidance controller is activated to prompt the vehicle to either decelerate or change its trajectory to prevent potential collisions. The CARLA simulator is used to train the proposed DRL collision avoidance controller, and virtual raw sensor data are utilized to enhance the realism of the simulations. The model-in-the-loop (MIL) methodology is utilized to assess the efficacy of the proposed DRL ADS routine. In comparison to the traditional DRL end-to-end approach, which combines high-level decision making with low-level control, the proposed hierarchical DRL agents demonstrate superior performance.

Real-Time Testing Optimal Power Flow in Smart-Transformer-Based Meshed Hybrid Microgrids: Design and Validation

The smart transformer (ST) is a multiport and multi-stage converter that allows for the formation of meshed hybrid microgrids (MHMs) by enabling AC-DC ports in medium and low voltage. This type of microgrid has advantages over the performance of conventional hybrid AC-DC microgrids (HMGs); however, the number of degrees of freedom of the ST increases the complexity of the energy management systems (EMSs), which require adequate and accurate modeling of the power flow of the converters and the MG to find the feasible solution of optimal power flow (OPF) problems in the MHM. An ST’s equivalent power flow model is proposed for formulating the MHM OPF problem and developing low-frequency equivalent models integrated with a decoupled hierarchical control architecture under a real-time simulation approach to the ST-based MHM. A simulation model of the MHM in the Simulink® environment of Matlab® 9.12 is developed and implemented under a digital real-time simulation (DRTS) approach on the OPAL-RT® platform. This model allows for determining the accuracy of the developed equivalent models, both low-frequency and power flow, and determining the MHM performance based on optimal day-ahead scheduling. Simulation test results demonstrated the ST equivalent model’s accuracy and the MHM’s accuracy for OPF problems with an optimal day-ahead scheduling horizon based on the model-in-the-loop (MIL) and DRTS approach.

A Fast Optimizing Method of Clutch–Clutch Shift Calibration Parameters

Aiming at the drawbacks of the heavy workload and lengthy calibration cycle of transmission control unit (TCU) development, a fast optimizing and test method of clutch–clutch shift calibration parameters is developed based on the “V” development process. A controlled-object simulation model of a six-speed automatic transmission (6AT) is established, and the shift calibration parameters and shift quality evaluation system are studied. Therefore, a model-in-the-loop (MIL) calibration platform for key calibration parameters and evaluation indicators in the shift process is constructed. By setting the optimization target of evaluation indicators, the initial calibration parameters’ ranges are reduced. Finally, the TCU is flashed with the optimized calibration parameters, and a hardware-in-the-loop (HIL) test system is built to verify the shift quality. The same shift quality verification test is carried out in the vehicle. The test results show that the application of the MIL simulation platform can enable the batch simulation analysis of eight key shift calibration parameters, gradually refine the initial rough interval, and ensure shift quality. The shift calibration parameters are cross-validated in multiple development links, and the dependence on vehicle calibration is greatly reduced. The development cycle is effectively shortened to less than 6 months.

Исследование предиктивных схем управления функционированием организационно-технических систем

Organizational and technical systems are the most common tool for solving national economic problems. However, due to the personnel included in them, they are characterized by stochastic behavior, which raises the issue of developing their probabilistic model. On the other hand, the probabilistic apparatus is a tool for forecasting future phenomena and situations and therefore a mechanism for its use in predictive (forecast) management is needed. For this purpose, the work uses the first probabilistic approximation in the form of a Markov process and its description by the Fokker–Planck–Kolmogorov equation. The purpose of the study is to formulate and solve optimal control problems with probabilistic control quality criteria for a tracking predictive control scheme, as well as to develop a mechanism for metasystem switching of mo¬dels in the case of an adaptive scheme with a predictive model in the control loop. Materials and methods. Based on the analysis of scientific ideas and methodological approaches in predictive management of domestic and foreign authors, as well as mathematical methods and models, two schemes for implementing predictive management were selected to improve the efficiency of the functioning of organizational and technical systems: tracking schemes and adaptive management. Results. The optimal control problem was solved using two methods: Pontryagin's maximum principle and Bellman's principle of dynamic programming. It is shown that in the first case there is a convex upward strategy for achieving the target value of the controlled value with a maximizing approach, and in the second case the result is a convex downward strategy used for the optimal consumption of management resources. In addition, for another predictive control scheme – adaptive with a predictive model in the loop, the effectiveness of using the strategy of metasystem switching of models with the formulation and solution of six metasystem problems has been proven. Conclusion. The proposed approach can be used when designing management systems in organizational and technical systems and choosing behavior strategies in difficult market conditions when solving problems of their development.

Research on Specific Scenario Generation Methods for Autonomous Driving Simulation Tests

In this paper, we propose a method for the generation of simulated test scenarios for autonomous driving. Based on the requirements of standard regulatory test scenarios, we can generate virtually simulated scenarios and functional scenario libraries for autonomous driving, which can be used for the simulated verification of different ADAS functions. Firstly, the operational design domain (ODD) of a functional scenario is selected, and the weight values of the ODD elements are calculated. Then, a combination test algorithm based on parameter weights is improved to generate virtually simulated autonomous driving test cases for the ODD elements, which can effectively reduce the number of generated test cases compared with the traditional combination test algorithm. Then, the traffic participant elements in each test case are sampled and clustered so as to obtain hazard-specific scenarios. Then, the values of the subelements under the traffic participant element in each test case are sampled and clustered to obtain hazard-specific scenarios. Finally, the specific scenarios are applied to the automatic emergency braking (AEB) system on the model-in-the-loop (MIL) testbed to verify the effectiveness of this scenario generation method.

Model-Based Calibration and Control of Tailpipe Nitrogen Oxide Emissions in a Light-Duty Diesel Engine and Its Assessment through Model-In-The-Loop

The present paper investigates two different strategies for model-based calibration and control of tailpipe nitrogen oxide emissions in a light-duty 3.0 L diesel engine equipped with an aftertreatment system (ATS). The latter includes a diesel oxidation catalyst (DOC), a diesel particulate filter (DPF), and an underfloor selective catalytic reduction (SCR) device, in which the injection of diesel exhaust fluid (DEF), marketed as ‘AdBlue’, is also taken into account. The engine was modeled in the GT-SUITE environment, and a previously developed model-based combustion controller was integrated in the model, which is capable of adjusting the start of injection of the main pulse and the total injected fuel mass, in order to achieve desired targets of engine-out nitrogen oxide emissions (NOx) and brake mean effective pressure (BMEP). First, a model-based calibration strategy consisting of the minimization of an objective function that takes into account fuel consumption and AdBlue injection was developed and assessed by exploring different weight factors. Then, a direct model-based controller of tailpipe nitrogen oxide emissions was designed, which exploits the real-time value of the SCR efficiency to define engine-out NOx emission targets for the combustion controller. Both strategies exploit the model-based combustion controller and were tested through a Model-in-the-Loop (MiL) under steady-state and transient conditions. The advantages in terms of tailpipe NOx emissions, fuel consumption, and AdBlue injection were finally discussed.

MIL, SIL and PIL Implementation for Closed Loop Control of Flyback Converter

Power electronic systems are rapidly evolving; thus, effective prototyping methods are required to test control algorithms and assess system performance prior to hardware implementation. This research suggests a Model-In-The-Loop (MIL), Software-In-The-Loop (SIL), and Processor-In-The-Loop (PIL) methodologies-based complete prototyping strategy for flyback converters using the TI F28069M Launchpad kit. These techniques can be combined to evaluate control strategies accurately and quickly, speeding up design cycles and enhancing system reliability. The proposed prototyping platform is presented in this work, along with a thorough explanation of each prototyping stage and its associated advantages. The effectiveness of the suggested approach for 50W rated power flyback converter in terms of quick algorithm development, system simulation, real-time control implementation and controller design accuracy is analyzed and shown by experimental results. The results show that the performance of the designed controller for the flyback converter is almost the same as in MIL and SIL implementation in terms of the overshoot and settling time in the reference voltage tracking. On the other hand, in the PIL implementation, the overshoot performance of the controller deviates by 1.18% more than in SIL and MIL implementation. These also confirm that the flyback control system's performance is reliable and effective during all phases of development.

Intelligent vehicle path tracking control strategy considering data-driven dynamic stable region constraints

The path tracking controller can easily reduce the tracking error, but often exceed the limitations of vehicle stability. In this paper, an intelligent vehicle path tracking control strategy considering data-driven dynamic stable region constraints is proposed. Firstly, based on the two-degree-of-freedom (DOF) vehicle model and nonlinear tire model, the vehicle sideslip angle-sideslip angular velocity ([Formula: see text]) phase plane is established. Then, the stable region dataset is made considering the influence of vehicle speed, adhesion coefficient, and front wheel angle. To get the vehicle driving stable region, a back propagation neural network (BP-NN) regression model is trained offline. Subsequently, a path tracking control strategy based on adaptive-model predictive control (MPC) is designed, which considers the vehicle dynamic stable region constraints with the BP-NN predicting online. Finally, model-in-the-loop (MIL) and driving simulator is designed to test the control strategy, which indicates that it has a better performance compared with the linear quadratic regulator (LQR) path tracking controller.

Optimal Control Design and Online Controller-Area-Network Bus Data Analysis for a Light Commercial Hybrid Electric Vehicle

In this article, a hybrid powertrain for the Volkswagen (VW) Crafter is designed using the Model-In-The-Loop (MIL) method. An enhanced Proportional-Integral (PI) control technique based on integral cost functions is developed by carrying out a time-based simulation in MATLAB/Simulink software to realize the optimal fuel economy of the vehicle. Moreover, a comparative study is conducted between the vehicle’s hybrid and pure electric versions to assess the optimal battery energy consumption per unit distance traveled. Communication within our vehicles’ Electronic Control Units (ECUs) is facilitated by a message-based protocol called a Controller Area Network (CAN). Consequently, this paper presents an online CAN Bus data analysis using the Hardware-In-The-Loop (HIL) method. This method uses a standard frame, J1939 CAN protocol, implemented with Net CAN Plus 110 hardware. A graphical user interface is developed on a host Personal Computer (PC) using LabVIEW for decoding the acquired raw CAN data to physical values. The simulation results reveal that the proposed controller is promising and suitable for realizing optimal performance over the HIL method.

Global stability of road vehicle motion with driver control

The paper contributes to unveil how drivers—either human or not—may lose control of road vehicles after a disturbance. First, a simple vehicle-and-driver model is considered: Its motion is characterized by the existence of limit cycles whose amplitude depend on vehicle forward velocity (both oversteering and understeering vehicles may exibit this property). Such limit cycles are originated by a Hopf bifurcation occurring at a relatively high vehicle forward velocity. A mathematical proof of the existence of Hopf bifurcations is given. The existence of Hopf bifurcations and saddle limit cycles is confirmed by experimental tests performed by a dynamic driving simulator with a complex vehicle model and human in the loop. By a Zubov method, a Lyapunov function is derived to compute the region of asymptotic stability for the simple vehicle-and-driver model. A necessary and sufficient condition is derived for global asymptotic stability. Such a condition refers to the variation of the kinetic energy which must vanish at the end of the disturbed motion. This occurrence has been detected at the driving simulator too. Just a single stable equilibrium has been found inside the domain of attraction in all of the examined cases.

Impedance‐matching model‐in‐the‐loop simulation

AbstractImpedance matching is a novel approach to executing model‐in‐the‐loop (MIL) simulations. The concept, key features, and sample applications of this approach have been described in a series of publications. This paper advances theory and implementation for impedance‐matching MIL by: (1) developing a feedback linearization technique for implementing MIL for nonlinear virtual systems, (2) systematically characterizing MIL performance‐stability tradeoffs using three design‐oriented criteria—performance: defined as closeness of the controlled‐actuator impedance to the virtual‐boundary impedance; control effort: low controller response at high frequencies to reduce sensitivity to measurement noise and uncertainties in the actuator model; and stability: pursued using passivity of the controlled actuator system, and (3) assessing, through experimentation, the robustness of two ways to estimate feedback reaction force from the test article. These fundamental advancements to MIL are described using a one‐dimensional configuration consisting of a fluid‐filled cylindrical vessel as the physical test article and a uniaxial hydraulic shake table controlled to imitate different seismic isolation systems at the base of the vessel.

Validation of automotive centrals using hardware in the loop -BCM and Lights

The race for electrification and the need for innovation to attract customers has driven the automotive industry to do something different in vehicles, emerging emission control challenges and efficient technological availability are the pillars of innovation. With the need to upgrade industrial manufacturing systems, directly impacting vehicle production, established the search for new prototyping methodologies and use of virtual tools for testing, validation of components and vehicle systems are developed. The demand for electronic components controls units (ECU) is growing, due to the availability of intelligence and security in today's vehicles, directly impacting their development and the performance and functionality tests. To meet this new reality, in addition to the automotive test prototypes used during development, the automotive industry has been using different virtual environments to produce, verify, and validate its vehicles. Therefore, this paper applied integration and validation using the HIL test platform, focusing on the ECU Body Control Unit (BCM), analyzing its functionalities and possible failures of the internal and external light loads. After, a brief commentary will be made exposing other test medium platforms given as the Plywood Buck (PWB), checking the reliability, flexibility, installation time, and costs of the three test platforms Software in the loop (SIL), Model in the loop (MIL) , Hardware in the loop (HIL), in order to verify their advantages, challenges, and problems in the use and information to optimize the use of each platform and test medium.

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

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

Visual based GNC system from prototype to flight software

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

An Effective Sensor Architecture for Full-Attitude Determination in the HERMES Nano-Satellites

The High Energy Rapid Modular Ensemble of Satellites (HERMES) is a constellation of 3U nano-satellites for high energy astrophysics. The HERMES nano-satellites' components have been designed, verified, and tested to detect and localize energetic astrophysical transients, such as short gamma-ray bursts (GRBs), which are the electromagnetic counterparts of gravitational wave events, thanks to novel miniaturized detectors sensitive to X-rays and gamma-rays. The space segment is composed of a constellation of CubeSats in low-Earth orbit (LEO), ensuring an accurate transient localization in a field of view of several steradians exploiting the triangulation technique. To achieve this goal, guaranteeing a solid support to future multi-messenger astrophysics, HERMES shall determine its attitude and orbital states with stringent requirements. The scientific measurements bind the attitude knowledge within 1 deg (1σa) and the orbital position knowledge within 10 m (1σo). These performances shall be reached considering the mass, volume, power, and computation constraints of a 3U nano-satellite platform. Thus, an effective sensor architecture for full-attitude determination was developed for the HERMES nano-satellites. The paper describes the hardware typologies and specifications, the configuration on the spacecraft, and the software elements to process the sensors' data to estimate the full-attitude and orbital states in such a complex nano-satellite mission. The aim of this study was to fully characterize the proposed sensor architecture, highlighting the available attitude and orbit determination performance and discussing the calibration and determination functions to be implemented on-board. The presented results derived from model-in-the-loop (MIL) and hardware-in-the-loop (HIL) verification and testing activities and can serve as useful resources and a benchmark for future nano-satellite missions.