Satellite Attitude Control System Research Articles

Feature-weighted Random Forest with Boruta for Fault Diagnosis of Satellite Attitude Control Systems

The performance of random forest (RF) based satellite attitude control system (ACS) fault diagnosis methods is limited by uninformative features in high-dimensional data. To solve this problem, we proposed a feature-weighted random forest with Boruta (FWRFB) based fault diagnosis method is proposed for fault diagnosis of ACSs. Firstly, a Boruta feature selection algorithm is used to obtain a feature set and determine significant feature weights. Subsequently, a novel feature-weighted random forest (FWRF) algorithm is designed, which utilizes feature-weighted random sampling instead of simple random sampling to generate feature subsets in the RF. The FWRFB effectively utilizes the feature information while mitigating noise interference. Finally, a FWRFB-based diagnostic module is developed for online fault diagnosis of ACSs. The effectiveness of the proposed method is verified by the ACS data from a semi-physical simulation platform.

Adaptive Multi-Model Predictive Control with Optimal Model Bank Formation: Consideration of Local Models Uncertainty and Stability

The Multiple Model Control (MMC) structure comprises three main components: the model bank, controller bank, and supervisor algorithm. Precise design of these components is crucial for achieving high control performance within the MMC framework, albeit this effort is not without its challenges. These challenges involve optimizing the model and controller banks ensuring system stability when dealing with uncertainties in the local models and enabling smooth switching between model-controller pairs. This paper addresses these challenges by presenting a comprehensive approach. Firstly, the optimal model bank is designed using an automatic clustering approach. Then, the design of the adaptive multi-model predictive controller bank and a supervisor algorithm capable of performing soft switching are discussed. The proposed control system exhibits the capability to ensure closed-loop system stability within each individual subspace, as well as during the transitioning between distinct subspaces. This stability is preserved even in the face of inherent uncertainties associated with the local models comprising the model bank. To evaluate and validate the performance of the proposed control system, it is applied to a satellite attitude control system. The results confirm the effectiveness and performance of the control system. The proposed control system holds promise for controlling highly nonlinear, complex, or switched systems, ensuring closed-loop system stability, and achieving high control performance.

Impact of proton radiation and annealing of CMOS image sensors on star sensor performance

Star sensors play a crucial role as high-precision optical attitude navigation devices in satellite attitude control systems. The CMOS image sensor is an important component of the imaging system of the star sensor and experiences performance degradation due to the total ionizing dose effect and displacement damage effect in the radiation environment of its long-term operational workspace. As a result, the detection capability and attitude positioning accuracy of the star sensor gradually deteriorate. The performance of CMOS image sensors can be partially restored after annealing but the impact on the performance of star sensors is unclear. This study conducted 100 MeV proton irradiation experiments with different fluences on CMOS image sensors and performed room temperature and high temperature annealing treatments. By testing and analyzing the performance of star sensors equipped with irradiated and different annealing treatments on CMOS image sensors, the impact mechanism of CMOS image sensor radiation damage and annealing recovery on the performance variation of star sensors was obtained. This study provides theoretical support for reliability of long-term in-orbit star sensors.

Optimal Synthesis of a Satellite Attitude Control System under Constraints on Control Torques and Velocities of Reaction Wheels

The problem of optimal synthesis of a nonlinear system’s control law parameters of satellite attitude control under constraints is considered. The maximum stability degree of a system is considered as an optimality criterion. The reaction wheels’ control moments and angular velocities achievable within the drives’ physical characteristics are considered as constraints. A linear model of the system converted from the original nonlinear model was used to solve the problem. The decomposition method, based on the transition from real time to relative time, is used to separate the tasks of optimality criterion maximization and constraints consideration. A method of synthesizing the optimal form of system transient process according to the maximum stability degree criterion is developed. An analytical method for determining the optimal values of control law parameters is proposed, based on the structural features of the linear differential equations system. A method that takes into account constraints on control moments and angular velocities of reaction wheels, based on transition scale from relative time to real time and its calculation algorithm, ensuring that conditions of all constraints are met, is developed. An example of solving the problem of optimal system synthesis is considered. The results demonstrate the effectiveness and simplicity of the proposed methods and algorithm for targeted selection of a system’s technical characteristics, taking into account constraints on reaction wheels’ maximum angular velocities and control moments values.

Characterization and verification of the optimal feedback gain of a satellite magnetorquer-based attitude control system

In spacecraft mission planning and operation, the attitude determination and control subsystem (ADCS) of a satellite provides information about the orientation of the satellite in the inertial reference frame. Furthermore, this subsystem produces the control actions required to adjust the orientation of the satellite, especially in the low-Earth orbit (LEO) regime. This paper focuses on the satellite’s three-axis attitude control problem within the context of active and passive control, which includes detumbling control, pointing control, magnetic control, and attitude stabilization after solar panel wing deployment using magnetorquers as the primary actuators. The objective is to stabilize and reduce the angular rate while orienting the satellite to the desired attitude. The proposed satellite attitude control system (ACS) strategies are designed, developed, characterized, and verified. These strategies encompass the B-dot control algorithm for detumbling control along with pointing control and attitude stabilization after solar panel wing deployment. hardware-in-the-loop simulation (HiLs) tests are conducted to assess the performance of the satellite magnetorquer-based ACS in the presence of noise. These tests involve a relative Earth’s magnetic field (EMF) generator in conjunction with SGP-4-based satellite orbital propagator high-level control software. Additionally, cascade proportional-integral-derivative (PID) and state-dependent Riccati equation (SDRE) controllers are implemented to generate sufficient torque using three-axis magnetorquers on a frictionless air-bearing platform. The platform is balanced to closely simulate the dynamic motion of a spacecraft in space. The testing includes a single initial condition and three inertia conditions for stabilization after solar panel wing deployment. Finally, the effectiveness of the cosimulation as a primary experiment through an integrated HiLs process is validated. This comprehensive approach confirms the control system’s performance and its ability to meet mission requirements.

Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system

Combined Energy and Attitude Control System (CEACS) reduces the size and mass budgets of typical satellites and consequently, increases their payload capacity. CEACS uses flywheels for a dual purpose, i.e., as both energy storage and attitude control device. This maiden work attempts to introduce a novel Deep-Learning capability of the fuzzy-Model Predictive Control (FMPC) controller for CEACS. The design approach for the fuzzy-MPC controller uses the Takagi-Sugeno (T-S) fuzzy model of satellite attitudes and computes the control torque through a parallel distribution compensation (PDC) approach. However, the MPC controller offers a high computational burden, and it becomes a significant problem for smaller satellites having limited computational power. Therefore, in this research work, a novel Deep-Learning-based fuzzy-MPC controller (D-FMPC) is designed for the CEACS attitude regulation subject to higher initial angles, actuator constraints, parametric uncertainties, and external disturbance torques. Here, the deep-layer neural network is trained offline with the MPC controller data to replicate the FMPC controller, thus ensuring its controllability. Numerical results validate that the D-FMPC controller successfully mimics the FMPC controller and produces the desired pointing accuracy effectively with smooth transient response and without violating the attitude control actuator constraints. The results also validate that the D-FMPC controller offers significantly reduced computational burden than the FMPC controller. Therefore, the novel Deep-Learning solution provides a feasible platform for applying more complicated and sophisticated attitude control techniques for the CEACS attitude regulation in small satellites as an example.

Automated Design of Fault Diagnosis CNN Network for Satellite Attitude Control Systems.

Despite the dominance of unsupervised and self-supervised anomaly detection methods in the current satellite fault diagnosis domain, supervised anomaly detection offers a superior alternative for high-sensitivity detection and lightweight deployment requirements specific to subsystems or components, such as attitude control systems (ACSs). This article addresses the issues of over-design and insufficient accuracy in the CNN network design for satellite ACS fault diagnosis by introducing the modified particle swarm optimization-advanced convolution blocks-based CNN (MPSO-ACBCNN) method. First, we present the ACBCNN, a lightweight, flexible-layer CNN architecture. This architecture leverages advanced convolution blocks (ACBs), which incorporate numerous efficient design elements to enhance feature extraction capabilities within power spectral density (PSD) graphs of various fault samples, and employs classical dense connection methods to prevent the issue of gradient vanishing. Second, we devise the MPSO-ACBCNN algorithm to optimize the ACBCNN fault diagnosis architecture for specified ACS using MPSO. In MPSO-ACBCNN, several optimizations to the canonical PSO are implemented, including the fitness design that balances the tradeoff between total parameter quantity and the training effectiveness, and methods to ensure feasible solutions, etc. Finally, numerical experimental results demonstrate the effectiveness and superiority of MPSO-ACBCNN in fault diagnosis for ACS.

Research on rapid batch test technology of small satellite attitude and orbit control system

The attitude and orbit control sub-system of small satellites is an important sub-system to ensure the stable operation of satellites in orbit, and its testing in the satellite factory development stage runs through the whole testing process. Most of the traditional attitude and orbit control sub-system tests require the cooperation of many people to complete the performance tests of various components and sensitive devices, which also leads to the complicated and time-consuming testing process of the sub-system. At present, the mass production of small satellites is developing rapidly. In order to meet the urgent needs of small satellite mass production, the traditional attitude control subsystem test equipment and methods can’t meet the requirements of satellite mass production mode. In order to meet the needs of small satellite mass development, this paper studies the rapid batch test technology of small satellite attitude and orbit control systems. A fast pipeline test system which can support the performance and dynamics simulation of various components of a multi-satellite attitude and orbit control subsystem is studied. Based on the architecture of real-time communication between the upper computer and lower computer, the system can realize the integration and rapid testing of component performance and dynamic simulation. A continuous dynamic simulation test of 2140 seconds was carried out for the cruise mode and attitude maneuver mode of a certain type of satellite in orbit. During the test, various components and sensors of the attitude and orbit control subsystem operate correctly and can support the stable operation of the required attitude in various modes of the satellite, which can verify the correctness of the interface and performance of the satellite attitude and orbit control subsystem. The research results greatly reduce the input of manpower and material resources, and greatly improve the test efficiency. It lays a foundation for the rapid batch test of the small satellite attitude control subsystem.

Improved time-variable transformer-based fault diagnosis method for satellite attitude control system

This study proposes a fault diagnosis method based on a time-variable transformer network for satellite attitude control system telemetry data, which has the properties of a time series and strong multivariate correlation. First, a time-attention module was utilised to determine the correlation between the data at a specific time and before that time to effectively identify the dynamic properties of the data. A variable attention module is introduced to capture the degree of correlation between various variables to achieve multivariate decoupling. Then, the interactive attention layer combines the dynamic properties of the data with the correlations between the variables to achieve dynamic data decorrelation. Finally, a linear layer is used to implement the fault diagnosis.

Situation modeling and evaluation for complex systems: A case study of satellite attitude control system

Because the dependence, competition, correlation, and other complex interactions among the components and between system and environment, the complex system is difficult to be modeled directly. As an important spacecraft system, satellite needs to meet the high reliability demand. However, the increasingly complex satellite system structure and complex space environment interference make the on-orbit satellite failure inevitable. Modeling and evaluation of satellite conditions can identify potential problems of key system components in time. Firstly, in order to accurately describe the system components and measure the reliability, the correlative damage coefficient is introduced to describe the component degradation process, and the component reliability model of the satellite attitude control system is established. Then, to solve the problem of large state space and changeable condition of system reliability model, the concept of tangent order pair is introduced into the multi-state dynamic fault tree of system. The depth-first search algorithm is used to sort the underlying event components of the fault tree, and the multi-state multi-value decision graph model of different state events is constructed. The minimum cut set of the model is obtained to evaluate the system state. The experimental results show that the proposed method can effectively simulate the system situation under different fault modes, effectively reduce the state space of the system reliability model, and improve the convergence and accuracy of the system fault probability evaluation.

A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode

The study of satellite performance evaluation can reveal the ability of satellite systems to fulfil corresponding tasks in the space environment, and provide information support for the resource allocation and mission scheduling of in-orbit satellites. In this paper, we took the satellite attitude control system in attitude tracking mode as the research object. In accordance with the system’s mission requirements, the control performance evaluation indicator set, characterized by a generalized grey number, is constructed to tackle the uncertainty and inadequacy of information contained in flight status data resulting from the complex space operating environment and sensor measurement noise. An improved principal component analysis method based on generalized grey number is proposed to solve the weight amplification caused by the correlation between performance indicators and realize the weight allocation of the indicators. Finally, the grey-target decision model is established to determine the weights of the performance indicators, and the performance evaluation model is established under the tracking mode. The feasibility of the grey-target decision-evaluation model based on the improved principal component is confirmed through comparative experiments.

Neuro‐Adaptive Finite Time Composite Fault Tolerant Control for Attitude Control Systems of Satellites

AbstractAn adaptive finite time composite fault tolerant control strategy based on an optimized neural network for Attitude control systems (ACSs) of satellites is proposed considering the state time‐varying delays, concurrent actuator and sensor faults, system uncertainties, modelable external disturbance and operating noise. An uncertain time‐varying state space model for ACSs of satellites is established, and sensor faults are equivalent to actuator‐like faults. A disturbance observer is designed for estimating the modelable external disturbance, and an improved dwarf mongoose optimization (DMO) algorithm based on the Levy flight distribution is utilized to optimize the basis function of hyperbasis function neural networks to better estimate the augmented actuator faults that include the actuator fault and the actuator‐like fault. Furthermore, an adaptive finite time composite fault‐tolerant controller is proposed, which includes the delay‐dependent feedback control law, disturbance estimation based‐disturbance compensation law and the adaptive fault compensation law based on the augmented fault estimation using the improved DMO‐hyper basis function neural network. The finite time boundness of the close‐loop dynamics to the uncertainties, operating noise, and augmented actuator faults and the robustness of the measurement to the uncertainties, operating noise and augmented actuator faults are analyzed, and the observer and controller design is formulated as the linear matrix inequalities. Simulation examples for ACSs in different working conditions are considered to exhibit the proposed method's effectiveness.

Real-Time Telemetry-Based Recognition and Prediction of Satellite State Using TS-GCN Network

With the continuous proliferation of satellites, accurately determining their operational status is crucial for satellite design and on-orbit anomaly detection. However, existing research overlooks this crucial aspect, falling short in its analysis. Through an analysis of real-time satellite telemetry data, this paper pioneers the introduction of four distinct operational states within satellite attitude control systems and explores the challenges associated with their classification and prediction. Considering skewed data and dimensionality, we propose the Two-Step Graph Convolutional Neural Network (TS-GCN) framework, integrating resampling and a streamlined architecture as the benchmark of the proposed problem. Applying TS-GCN to a specific satellite model yields 98.93% state recognition and 99.13% prediction accuracy. Compared to the Standard GCN, Standard CNN, and ResNet-18, the state recognition accuracy increased by 37.36–75.65%. With fewer parameters, TS-GCN suits on-orbit deployment, enhancing assessment and anomaly detection.

Flywheel Vibration Isolation of Satellite Structure by Applying Structural Plates with Elastic Boundary Instead of Restrained Boundary

Flywheels play a critical role as core components in satellite attitude control systems. However, their high-speed rotation inevitably generates vibrations that have a detrimental impact on the in-orbit imaging capabilities of high-precision remote sensing payloads. This study focuses on the passive vibration isolation design of satellite flywheels. The flywheel-mounted structural plate and flywheel vibration isolation platform are considered as a whole system (termed a plate-isolator system). In this system, the structural plate is treated as an elastomer. By simplifying the plate-isolator system as a 2-degree-of-freedom vibration system, it becomes evident that obtaining an ideal vibration isolation effect through the optimization of the flywheel vibration isolation platform (FVIP) alone is difficult. In order to enhance the passive vibration isolation effect for satellite flywheels, this study introduces the concept of an elastic boundary applied to the flywheel-mounted structural plate, thus treating the elastic boundary as a design factor. Consequently, the plate-isolator system can be simplified as a 3-stage vibration isolation system. The optimization of the elastic boundary condition of the structural plate is performed using the kinetic model of the simplified 3-stage system. The vibration isolation effect of the plate-isolator system with an elastic boundary is further confirmed through finite element simulation. The calculation results demonstrate that, after establishing a reasonable elastic boundary for the satellite structural plate, the overall vibration/force transmission rate of the plate-isolator system becomes similar to that of a single-degree-of-freedom dynamic system. Finally, the proposed concept is validated through kinetic response analysis of a cube satellite. The results reveal that the vibration amplitude of the satellite’s top and side structural plates can be effectively lowered if the elastic boundary condition is set for the flywheel-mounted bottom structural plate.

Parameters identification of the satellite attitude control system with large inertia rotating components

This paper investigates the unbalance parameter identification of the large inertia rotating component of satellite. Firstly, the dynamics model with unbalance parameter of the large inertia rotating component is established. Then, based on the principle of parameter separation and decoupling, a modified two-stage exogenous Kalman filter (TSXKF) algorithm is proposed. This method works directly on nonlinear system, estimates the centroid position, the centroid velocity, attitude angular, attitude angular velocity, and identifies the nonlinear unknown static unbalance parameter, which is the centroid offset. Finally, the simulation results verify the effectiveness of the method.

Modeling and analysis of the influence caused by micro-vibration on satellite attitude control system

A closed-loop integrated model including micro-vibration disturbance, dynamic characteristics of satellite, optical system of star sensor and attitude control algorithm is established. The control torque error caused by additional disturbance torque and the star sensor imaging error caused by structural resonance is fully integrated analyzed, which are firstly presented into the attitude control system (ACS). Steady-state and transient response analyses are completed to evaluate the impact of such two errors on performances of (ACS). Steady-state response results show that the effect of control torque error appears in lower frequency band with a peak of 6.784E-8″ at 53 Hz, while influence of star sensor imaging error on attitude determination mainly concentrates in high frequency band with maximum response of 0.4101″ at 315 Hz. Transient response results show that the two errors will not significantly affect control indicators such as rise time, overshoot and peak time. The results indicate that the influence of star sensor imaging error is non-negligible for satellite ACS which required milli-arc-second attitude control accuracy. According to the analytical results, traditional analysis only on payload optical axis cannot reveal the whole impact of micro vibration on LOS error, the two errors of micro-vibration introduced to ACS in this work must be considered for the development of spacecraft with 0.1″ pointing accuracy.

Combined Fault Diagnosis Scheme Based on Recurrent Neural Network and H− Observer for Satellite Attitude Control System

This study proposes a combined fault diagnosis scheme based on a recurrent neural network (RNN) and an H− observer for satellite attitude control systems (ACSs) in the presence of model uncertainties, external disturbances, and measurement noise. The ACS is decoupled into multiple independent channels, such that the residuals generated by the observers respond to the corresponding faults. A novel multilayer adaptive Gaussian recurrent neural network (MAGRNN) structure is developed as an approximator to estimate the lumped disturbance; a robust term is introduced to improve accuracy. Considering the actuator fault in the finite frequency domain and the fault-sensitive index, a set of H− unknown input observers (UIOs) is designed using the output of the MAGRNN-based approximator as compensation. The existence conditions of the approximator and observer are proposed and proved. The fault diagnosis results for three cases verify that the proposed method can be used for small fault detection and isolation.

Robust nonlinear MPC without terminal constraint for tracking changing setpoints in the presence of non‐additive unknown disturbance

AbstractThis paper develops a novel robust tracking model predictive control (MPC) without terminal constraint for discrete‐time nonlinear systems capable to deal with changing setpoints and unknown non‐additive bounded disturbances. The MPC scheme without terminal constraint avoids difficult computations for the terminal region and is thus simpler to design and implement. However, the existence of disturbances and/or sudden changes in a setpoint may lead to feasibility and stability issues in this method. In contrast to previous works that considered changing setpoints and/or additive slowly varying disturbance, the proposed method is able to deal with changing setpoints and non‐additive non‐slowly varying disturbance. The key idea is the addition of tightened input and state (tracking error) constraints as new constraints to the tracking MPC scheme without terminal constraints based on artificial references. In the proposed method, the optimal tracking error converges asymptotically to the invariant set for tracking, and the perturbed system tracking error remains in a variable size tube around the optimal tracking error. Closed‐loop input‐to‐state stability and recursive feasibility of the optimization problem for any piece‐wise constant setpoint and non‐additive disturbance are guaranteed by tightening input and state constraints as well as weighting the terminal cost function by an appropriate stabilizing weighting factor. The simulation results of the satellite attitude control system are provided to demonstrate the efficiency of the proposed predictive controller.

Dynamics Analysis of a Nonlinear Satellite Attitude Control System Using an Exact Linear Model

This study aims to analyze the nonlinear dynamics of a satellite attitude control system equipped with reaction wheels and a PD controller. Based on the angular momentum conservation theorem for a closed mechanical system, the nonlinear equations of the attitude control system dynamics are presented as a linear system of differential equations with time-varying parameters. The asymptotic properties of the angular momentum of a mechanical system including a satellite and reaction wheels as rigid bodies are investigated. A relation has been established between the dynamic parameters of the attitude control system and the initial value of the angular momentum of the satellite. The issue of asymptotic stability for differential equations with time-varying parameters is simplified to the asymptotic stability problem for the ultimate homogeneous system of linear differential equations with constant elements. The dependencies of the dynamic parameters of the attitude control system on the constant parameters of this ultimate system of linear differential equations, as well as the initial values of the satellite’s angular momentum, enable us to apply proven and effective engineering methods. These methods are used not only for analyzing the stability of the control system but also for synthesizing the parameters of the control law based on the quality requirements of transient processes such as the stability margin, responsiveness, oscillation, transient time, and overshoot. In this case, the calculation of the control law parameters will be grounded in exact equations, not on approximate equations of the control system dynamics obtained by linearization.

Online Network-Based Identification and its Application in Satellite Attitude Control Systems

Most satellite attitude control models are built analytically, which requires a clear understanding of the kinematic equations of the satellites and the various disturbance models to deal with the interferences and uncertainties in the space environment. This paper studies a system identification method to build a system model for satellite attitude control. The satellite model is based on a neural network model, i.e., the RBF-ARX model, which requires only observation data. Unlike the existing off-line identification methods for the RBF-ARX model, this paper proposes an on-line identification method that can constantly correct the model according to the newest data. To reduce the computational complexity of identifying the satellite model, a decomposition scheme is also developed. The computational efficiency of the proposed method is analyzed. Finally, the efficacy of the proposed identification method is demonstrated by applying it to two satellite attitude control systems.