Attitude Control System Research Articles

USLC: Universal self‐learning control via physical performance policy‐optimization neural network

AbstractThis article proposes an online universal self‐learning control (USLC) algorithm based on a physical performance policy‐optimization neural network, which aims to solve the problem of universal self‐learning optimal control laws for nonlinear systems with various uncertain dynamics. As a key system characterization, this algorithm predicts the discrepancy between the optimal and current control laws by evaluating overall performance in each iterative learning cycle, leveraging an offline‐trained universal policy network. This approach is universal, as it does not rely on an exact system model and can adaptively control performance preferences across various tasks by customizing the physical performance cost weights. Using the established control law‐performance surface and contraction Lyapunov function, the necessary assumptions and proofs for the stable convergence of the system within a three‐dimensional manifold space are provided. To demonstrate the universality of USLC, simulation experiments are conducted on two different systems: a low‐order circuit system and a high‐order variable‐span aircraft attitude control system. The stable control achieved under varying initial values and boundary conditions in each system illustrates the effectiveness of the proposed method. Finally, the limitations of this study are discussed.

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.

Attitude Control System for Quadrotor Using Robust Monte Carlo Model Predictive Control

Monte Carlo Model Predictive Control (MCMPC) is a kind of non-linear Model Predictive Control (MPC) that determines control inputs using the Monte Carlo method. The Monte Carlo method used in MCMPC can handle discontinuous phenomena, and there have been reports of its application in control systems for quadrotors, where the objective is to maintain the stability of the attitude during collisions. However, as with conventional MPC, concerns remain about control performance degradation due to modeling errors and external disturbances such as unexpected wind gusts. In this study, we propose a novel Robust Monte Carlo Model Predictive Control (RMCMPC), which improves the robustness of MCMPC by considering the hypersurface known from the Sliding Mode Control (SMC) theory. The proposed RMCMPC is applied to a quadrotor attitude control system, and its effectiveness is validated through numerical simulations.

Improved Adaptive Sensor Fault Recovery for an Attitude Control System Simulator Using a New Terminal Sliding Mode Controller with Faster Convergence and Guaranteed Performance

Improved Adaptive Sensor Fault Recovery for an Attitude Control System Simulator Using a New Terminal Sliding Mode Controller with Faster Convergence and Guaranteed Performance

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.

Study of the features of angular stabilization of spacecraft with flexible struc-tural elements with the use mobile control methods

The development of space power engineering is one of the well-known lines in rocket and space science and innovative technologies which attracts the attention of many scientists and researchers. Engineering solutions in space-based solar power plant design and wireless space-to-Earth and satellite-to-satellite power transmission and power spacecraft control methods have been substantiated theoretically to sufficient depth. However, despite this, there is a need to improve methods for and approaches to the development of an optimal design methodology for power spacecraft. A way to improve existing approaches to the development of space-based solar power plants and power satellites may be the use of mobile control methods in the development of an attitude and orbit control system. Such methods allow one to reduce power consumption for control operations. The goal of this paper is to study the features of mobile control and construct a methodology for the development of solar power satellites’ attitude and orbit control system (AOCS) using mobile control algorithms. The paper considers the features of mobile control algorithm synthesis for the attitude control and stabilization of solar power spacecraft (solar power plants and power satellites). Power spacecraft control tasks are classified, and the expediency of using mobile control methods is justified. An analysis is made for the stability problem that arises in controlling power spacecraft with flexible elements. The paper presents methodological recommendations on determining the AOCS design parameters for space-based solar power plants and power spacecraft for wireless satellite-to-satellite power transmission. This methodology may be used in power satellite development.

Swiftly Chasing Gravitational Waves across the Sky in Real Time

We introduce a new capability of the Neil Gehrels Swift Observatory, dubbed “continuous commanding,” that achieves 10 s latency response time on orbit to unscheduled target-of-opportunity requests received on the ground. We show that this will allow Swift to respond to premerger (early-warning) gravitational-wave (GW) detections, rapidly slewing the Burst Alert Telescope (BAT) across the sky to place the GW origin in the BAT field of view at or before merger time. This will dramatically increase the GW/gamma-ray burst (GRB) codetection rate and enable prompt arcminute localization of a neutron star merger. We simulate the full Swift response to a GW early-warning alert, including input sky maps produced at different early-warning times, a complete model of the Swift attitude control system, and a full accounting of the latency between the GW detectors and the spacecraft. 60 s of early warning can double the rate of a prompt GRB detection with arcminute localization, and 140 s guarantees observation anywhere on the unocculted sky, even with localization areas ≫1000 deg2. While 140 s is beyond current GW detector sensitivities, 30–70 s is achievable today. We show that the detection yield is now limited by the latency of LIGO/Virgo cyberinfrastructure and motivate a focus on its reduction. Continuous commanding has been integrated as a general capability of Swift, significantly increasing its versatility in response to the growing demands of time-domain astrophysics. We demonstrate this potential on an externally triggered fast radio burst (FRB), slewing 81° across the sky, and collecting X-ray and UV photons from the source position <150 s after the trigger was received from the Canadian Hydrogen Intensity Mapping Experiment, thereby setting the earliest and deepest such constraints on high-energy activity from nonrepeating FRBs. The Swift Team invites the community to consider and propose novel scientific applications of ultra-low-latency UV, X-ray, and gamma-ray observations.

In-orbit demonstration of acquisition and tracking on OSIRIS4CubeSat

OSIRIS4CubeSat is the smallest commercially available laser communication terminal within the confines of 0.3-units. It was launched as PIXL-1 inside of a 3-unit CubeSat into space to demonstrate optical direct to earth links. This work primarily focuses on the commissioning phase, particularly emphasizing the performance of the active fine pointing and tracking mechanism. This steering mechanism, with a 1 degree radius field of regard, plays a decisive role in compensating for pointing inaccuracies from the satellite’s attitude determination and control system. The design and validation of open-loop acquisition and closed-loop tracking are presented. The feedback sensor adapts to changing beacon power levels, that occur during a direct to earth link, by an adaptive gain control. Subsequent evaluation of satellite passes, with telemetry recordings obtained from the deployed CubeSat, showcases superior performance that meets the pointing requirements of the link budget. By using the developed control-loop logic, OSIRIS4CubeSat achieves a mean tracking error of 71 µrad, with a 3σ deviation of 140 µrad down to low power levels of 238 pW. These results serve as validation of OSIRIS4CubeSat, demonstrating its capability to enable high-speed data transmission from low Earth orbit to the Earth’s surface at data rates of up to 100 Mbit/s.

Design of an Optimal Allocator for Power Consumption Minimization in Hexarotor Drone Control Systems

This paper presents an allocator design that considers the power consumption of rotors in the attitude and altitude control system of a hexarotor drone. Based on the power consumption model, the proposed method computes the thrust force that minimizes the total power consumption of the rotor while satisfying the control force constraints required by the controller. To obtain the rotor power consumption model, we conducted experiments on the rotor characteristics using the motors and electronic speed controllers used in the drones. Finally, numerical simulations were performed using the obtained power consumption models to compare the rotor power consumption of the proposed method with that of the conventional method, quantitatively evaluating the effectiveness of the proposed method.

Adaptive predefined-time precise anti-unwinding attitude control for a spacecraft with inertia uncertainty

This paper proposes an adaptive predefined-time accurate anti-unwinding attitude controller for a spacecraft with uncertain inertia. The novelty of the proposed algorithm lies in that the spacecraft attitude error is regulated to exact zero after a predefined settling time, which is an explicit parameter in the control algorithm. A time mapping function containing the predefined settling time is introduced, thus transforming the time scale of the original attitude control system. Then a composite barrier Lyapunov function is constructed to avoid the unwinding phenomenon in the attitude control process, and an adaptive backstepping attitude controller is designed based on the attitude system in the new time scale. The stability analysis of the attitude system is carried out based on LaSalle-Yoshizawa theorem, and it is proved that the attitude quaternion and angular rate can converge to zero accurately within the desired predefined time without using the spacecraft inertia information. Numerical simulation results show the effectiveness and superiority of the proposed controller.

Reliability modeling of multi-state phased mission systems with random phase durations and dynamic combined phases

Random phase durations and dynamic combined phases challenge the application of existing reliability models in the reliability analysis of multistate-phased mission systems (MS-PMSs). To this end, this paper presents a new reliability modeling method for multi-state phased mission systems with random phase durations and dynamic combined phases. Initially, a multi-state multi-valued decision diagram-based (MMDD-based) reliability modeling method is created to efficiently map random phase durations and the dynamic combined phase nature of MS-PMSs into the reliability model. To solve the MMDD-based reliability model, a path probability evaluation method is subsequently constructed with the assistance of the Markov regenerative function. The effectiveness and the superior performance of the proposed MMDD-based reliability model and its solving algorithm are validated by the application to the reliability modeling and analysis of an attitude and orbit control system with multiple modes. Overall, this paper provides the reliability sector with a new reliability model and its solving algorithm to enhance the reliability and safety of multi-state phased mission systems.

A Model-Free Adaptive Positioning Control Method for Underactuated Unmanned Surface Vessels in Unknown Ocean Currents

Aiming to address the problem of underactuated unmanned surface vehicles (USVs) performing fixed-point operations at sea without dynamic positioning control systems, this paper introduces an original approach to positioning control: the virtual anchor control method. This method is applicable in environments with currents that change slowly and does not require prior knowledge of current information or vessel motion model parameters, thus offering convenient usability. This method comprises four steps. First, a concise linear motion model with unknown disturbances is proposed. Then, a motion planning law is designed by imitating underlying principles of ship anchoring. Next, an adaptive disturbance observer is proposed to estimate uncertainties in the motion model. In the last step, based on the observer, a sliding-mode method is used to design a heading control law, and a thrust control law is also designed by applying the Lyapunov method. Numerical simulation experiments with significant disturbances and tidal current variations are conducted, which demonstrate that the proposed method has a good control effect and is robust.

A novel smooth quaternion-based attitude tracking control

The fact that two antipodal points in the quaternion characterize the same physical attitude depicts that the design of the global attitude control system is uneasy. This study deals with a smooth quaternion-based attitude tracking control system that is composed of a differential equation known as the augmented dynamical system. The augmented dynamical system plays such an important role that it is possible to conduct a novel perspective on viewing the set containing all possible motions as not a manifold. Therefore, the topological obstruction does not exist. The designed attitude tracking control system has a guarantee of stability in the large within a set containing all possible motions. It is able to avoid the unwinding phenomenon, and it is noise-induced chattering-free since there is no discontinuity. Furthermore, we show that the smooth attitude tracking control system has an integral input-to-state stability (iISS) guarantee in the presence of uncertainties in the moment of inertia and environmental disturbance. Simulations carried out show the effectiveness of the designed attitude tracking control system.

Self-triggered MPC with adaptive prediction horizon for nano-satellite attitude control system

Nano-satellites are essential tools for various applications, including scientific experiments, deep space exploration and astronomical observation. Achieving precise model predictions is crucial for their successful operation. To address the intricate constraints of nano-satellites and enhance control performance, the Model Predictive Control (MPC) algorithm is an effective solution. However, implementing an MPC-based attitude control system in actual engineering scenarios presents significant challenges, primarily due to the substantial computational burden, especially given the limited onboard computing resources of nano-satellites. In this paper, we introduce a modified adaptive self-triggered model predictive control (ST-MPC) algorithm designed to stabilize the attitude of nano-satellites, while simultaneously reducing communication and computational overhead compared to traditional MPC methods. The proposed self-triggered mechanism dynamically determines the next trigger time according to the system state. Moreover, we incorporate considerations for the efficiency of actuators to address the constraints imposed by the magnetic torque characteristics within the modified self-triggered mechanism. Additionally, a strategy for adaptive prediction horizon is proposed to balance computation load and control accuracy. The results of our simulations demonstrate the effectiveness of the modified ST-MPC algorithm in comparison to both traditional MPC and standard ST-MPC approaches. This algorithm may have the potential to significantly impact attitude control applications for nano-satellites.

Fabrication Time Diagrams for In-Space Manufacturing of Large Reticulated Structures

Abstract In-space manufacturing (ISM), or the on-orbit construction of structures from raw feedstock materials, is a promising approach for building large structures for future space missions. Most research and development on ISM to date has focused on ground-based or microgravity-based demonstrations of candidate fabrication processes; however, the combined design of the fabrication process and the ISM spacecraft has not been fully investigated. In this paper, we estimate the fabrication times for ISM of reticulated support structures subject to various spacecraft constraints, including the available fabrication power, the available attitude control authority, and the avoidance of undesirable control-structure interactions. We summarize the insights in fabrication time diagrams which depict the dominant constraints and estimates of the fabrication time for a wide range of structural geometries. Our results indicate that for large, dense structural geometries such as the curved gridshell and tetrahedral truss, the angular momentum storage of the ISM spacecraft's attitude control system (ACS) is the dominant constraint on fabrication time. Additionally, our results suggest the following strategies for reducing fabrication time: manufacturing with multiple spacecraft; using stiff, lightweight feedstock; maximizing fabrication power and ACS capability; and minimizing spacecraft bus mass. Altogether, our results emphasize how the design of an ISM spacecraft cannot be considered independently of the fabricated structure and motivate future work on selecting optimal fabrication paths and attitude control strategies to fabricate large structures in the shortest time.

On-orbit satellite hierarchical anomaly detection using causal structure learning

Real-time anomaly detection for on-orbit satellites is crucial in the early identification of faults to prevent further anomaly expansion. However, current anomaly detection methods for on-orbit satellites face challenges such as low sensitivity to anomalies, high rates of false negatives, and a lack of criteria for assessing anomaly severity. The Spatio-Temporal Causal Graph (STCG) describes the intrinsic temporal and spatial relationships among telemetry parameters. By extracting temporal and spatial features, it facilitates enhanced modeling of normal data and improves the model’s sensitivity to anomalies. Moreover, according to the principle of anomaly propagation, the STCG can be utilized to identify false negatives. Therefore, we propose a framework of on-orbit satellite hierarchical anomaly detection using causal structure learning (OSHAD-CSL). This framework introduces a graph autoencoder for STCG learning and develops a method for dynamically setting detection thresholds. Additionally, the framework puts forward a false negative identification method based on the learned STCG. Lastly, the framework presents an anomaly severity judgment criterion, enabling the determination of the affected system level by the detected anomaly. To evaluate the effectiveness of the proposed framework, we validate the OSHAD-CSL on three public datasets and a dataset from a satellite’s attitude control system. The results demonstrate that the proposed method outperforms the baselines, with an F1-score (a metric used for evaluating the performance of anomaly detection) of 80% for anomaly detection and an accuracy of 83% for anomaly severity judgment. These findings highlight the efficacy of the proposed method in improving the performance of anomaly detection and severity assessment.

Incremental Sliding Mode Control for Predefined-Time Stability of a Fixed-Wing Electric Vertical Takeoff and Landing Vehicle Attitude Control System

Incremental Sliding Mode Control for Predefined-Time Stability of a Fixed-Wing Electric Vertical Takeoff and Landing Vehicle Attitude Control System

Command-filtered incremental backstepping attitude control of spacecraft with predefined-time stability

This paper investigates the predefined-time attitude tracking control problem within the theoretical framework of incremental dynamic inversion. The utilization of the Taylor series facilitates the transformation of the attitude control system into a discrete-time plant, with the control input expressed in an incremental form. Additionally, a novel predefined-time stable dynamics system is presented and incorporated into the disturbance observer designing process, serving the purpose of estimating lumped disturbance. It is also employed in a command filter design to approximate the derivatives of the virtual control law within a predefined time. Consequently, an incremental backstepping attitude tracking control scheme is further developed, integrating the proposed predefined-time disturbance observer to ensure the system's robustness and the predefined-time filter to address challenges related to “term explosion” and singularity problem. Rigorous Lyapunov analysis affirms that the attitude control system, when using the incremental backstepping controller, remains predefined-time stable. The effectiveness of the proposed control scheme is subsequently validated through numerical simulations.

Gaussian-inverse gamma mixture distribution–based extended Kalman observer for dynamic positioning control system of offshore platform

Under different working conditions and control objectives, fuzzy model predictive control is used to optimize the control effect by adjusting the weight to achieve stable and accurate control for the dynamic positioning control of offshore platforms. Also, aiming at the situation where the measurement noise of dynamic positioning control may be time-varying and non-Gaussian and the statistics of the noise are not exact, this paper proposes a variational Bayesian extended Kalman filter observer. In order to model non-Gaussian noise more reasonably, the joint posterior distribution of non-Gaussian noise with state and time-varying covariance is described as the product of Gaussian and independent inverse gamma distributions. Then, the variational Bayesian (VB) method was used to simultaneously estimate the system state, the intermediate variable values of the new distribution, and the noise parameters through fixed-point iteration. The fuzzy algorithm is applied to the model predictive control, and the weight parameters of the model predictive control are adaptively changed to better adapt to the changing working conditions in the control process. The simulation results show that the variational Bayesian extended Kalman filter observer has better performance than the existing algorithms when dealing with the time-varying non-Gaussian observation noise, whose statistics are not exact. The variational Bayesian extended Kalman filter observer can significantly improve the accuracy of the control when it is used in fuzzy model predictive control.

Robust neuro-adaptive command-filtered back-stepping fault-tolerant control of satellite using composite learning

This study proposes an adaptive neural command-filtered backstepping control system for precise and fast attitude control of a satellite considering different uncertain dynamics. Mission success crucially depends on robust attitude control resilient to model uncertainties, unmodeled dynamics, external disturbances, and actuator faults. The proposed approach synergistically combines a neural network for handling model uncertainties, unmodeled dynamics, and actuator faults, with a disturbance observer for compensating external disturbances and neural network estimation errors. The command-filtered backstepping technique avoids the explosion of complexity inherent to traditional backstepping, while integrating integral action into the design results in the elimination of the steady-state tracking error. Besides, a composite learning method optimizes the update laws for the neural network and disturbance observer weights, enhancing control performance. Despite the presence of uncertainties, the closed-loop system stability is guaranteed by the Lyapunov stability theorem. Simulation results demonstrate the proposed controller’s ability to handle severe actuator faults, unmodeled dynamics, and measurement noise without requiring explicit fault detection and isolation schemes.