This study introduces a robust model-based framework designed for the verification and validation (V&V) of Attitude Determination and Control Systems (ADCSs) in nanosatellites, focusing on magnetic actuation while still being applicable to larger spacecraft platforms. By employing Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), Processor-in-the-Loop (PIL), and Hardware-in-the-Loop (HIL) methodologies, this framework enables a thorough and systematic approach to testing and validation. The framework facilitates the assessment of long-term maneuvers, addressing challenges such as initial small-attitude errors and restricted 3D movements. Two specific maneuvers are evaluated: detumbling and nadir pointing, utilizing quaternions and a comprehensive suite of sensors, including six sun sensors, a three-axis magnetometer, a three-axis gyroscope, GPS, and three magnetorquers. The methodologies—MIL, SIL, PIL, and HIL—integrate the behaviors of digital sensors, analog signals, and astrodynamic perturbations. Based on an optimized SIL environment, Monte Carlo simulations were performed to optimize control gains for nadir pointing, achieving a mean pointing accuracy of 11.69° (MIL) and 18.22° (PIL), and an angular velocity norm of 0.0022 rad/s for detumbling. The HIL environment demonstrated a mean pointing accuracy of 9.96° and an angular velocity norm of 0.0024 rad/s. This comprehensive framework significantly advances the design and verification processes for nanosatellite ADCSs, enhancing the reliability and performance of nanosatellite missions.

Nanosatellite technology, particularly CubeSats 1U, has been promoted in recent years by the development of commercial-of-the-shelf (COTS) components which, due to their reduced size and weight, represent lower cost and development time. However, the reliance on COTS increases the susceptibility of nanosatellites to failures due to extreme environmental conditions encountered in space, such as ionizing radiation, temperature, and albedo. Therefore, it is necessary to quantify the impact that environmental variables have on the functionality of COTS components like the BPW34 (Coarse Sun Sensor), which is used to determine the sun vector for attitude determination and control, to determine if its use is feasible for missions. This study focuses on quantitatively evaluating the impact of environmental conditions and the ionizing radiation dose rate (captured with the Geiger Counter SEN0463) on the performance of the BPW34 sun sensor through regression analysis and correlation matrices using empirical data collected up to an altitude of 17 [km] aboard a stratospheric balloon.

Anomaly signals in the Attitude Determination and Control System (ADCS) of nanosatellites can significantly degrade mission performance, especially in the absence of robust fault detection, isolation, and recovery (FDIR) mechanisms. Thus, traditional threshold-based approaches, while portable and compact, may overlook subtle faults, whereby abnormal sensor signals or current spikes within the threshold may compromise the operation of the entire ADCS as a subsystem. Furthermore, the lack of interpretable detection methods further limits the development of reliable machine learning (ML) FDIR solutions. To address these limitations, this work presents a wavelet-based anomaly detection framework that introduces a two-stage hybrid architecture combining a lightweight Convolutional Neural Network (CNN) for fault detection with logistic regression for fault classification, both based on Discrete Wavelet Transform (DWT) detail coefficients extracted from sensor and actuator data. The framework was validated using a statistics-based anomaly dataset for a 1U CubeSat ADCS simulated in MATLAB, in which anomalies are introduced at the component level with controlled variations in magnitude, frequency, and waveform, ensuring 99% statistical significance. Additionally, to demonstrate operational feasibility, constraints for onboard implementation were considered by executing the proposed framework in a Processor-in-the-Loop (PIL) environment. For benchmarking, lightweight detection and classification algorithms were compared, including Out-Of-Limits (OOL) and compact machine learning approaches. Finally, to identify the framework’s limitations and trace faulty events to physical phenomena, Grad-CAM, SHAP, and impurity analysis were performed on the proposed algorithms as primary interpretability tools. Consequently, the results demonstrate accurate fault detection and identification to support both autonomous FDIR actions and ground operator decision-making. The proposed validation framework and dataset provide a reproducible basis for advancing anomaly detection onboard nanosatellites.

Anomaly signals in the Attitude Determination and Control System (ADCS) of nanosatellites can significantly degrade mission performance, especially in the absence of robust Fault Detection, Isolation, and Recovery (FDIR) mechanisms. Thus, traditional threshold-based approaches, while portable and compact, may overlook subtle faults, whereby abnormal sensor signals or current spikes within the threshold may compromise the operation of the entire ADCS as a subsystem. Furthermore, the lack of interpretable detection methods further limits the development of reliable machine learning (ML) FDIR solutions. To address these limitations, this work presents a wavelet-based anomaly detection framework that introduces a two-stage hybrid architecture combining a lightweight Convolutional Neural Network (CNN) for anomaly detection with logistic regression for fault classification, both based on discrete wavelet transform (DWT) detail coefficients extracted from sensor and actuator data. The framework was validated using a statistics-based anomaly dataset for a 1U CubeSat ADCS simulated in MATLAB, in which anomalies are introduced at the component level with controlled variations in magnitude, frequency, and waveform, ensuring 99% statistical significance. Additionally, to demonstrate operational feasibility, constraints for onboard implementation were considered by executing the proposed framework in a Processor-in-the-Loop (PIL) environment. For benchmarking, lightweight detection and classification algorithms were compared, including Out-Of-Limit (OOL) and compact machine learning approaches. Finally, to identify the framework’s limitations and trace faulty events to physical phenomena, Gradient-weighted Class Activation Mapping (Grad-CAM), SHapley Additive exPlanations (SHAP), and impurity analysis were performed on the proposed algorithms as primary interpretability tools. Consequently, the results demonstrate accurate anomaly detection and identification to support both autonomous FDIR actions and ground operator decision-making. The proposed validation framework and dataset provide a reproducible basis for advancing anomaly detection onboard nanosatellites.