Thruster Fault Research Articles

Reconfigurable Guidance Strategy for Compensating Actuator Faults in Spacecraft Formation Flying

The capacity to keep a desired topology with a requested accuracy plays a significant role in every spacecraft formation-flying operation. These missions can be terminated in case of an unexpected spacecraft fault, preventing the system from returning to its nominal configuration. This paper presents and tests a new recovery solution, called Reconfigurable Guidance Strategy (RGS), for the spacecraft formation-flying control problem subject to a look-in-place permanent thruster fault. The proposed method relies on autonomously and in real-time reconfiguring the guidance function to compensate for the loss of the spacecraft actuation system. The performance and cost of the RGS have been tested in a high-fidelity simulation scenario, the 42 spacecraft simulator developed by NASA Goddard Space Flight Center, taking into account orbital and rotational nonlinear coupled dynamics, high-order perturbation models, and actuator and sensor models. The numerical simulation results have demonstrated the proposed recovery strategy’s effectiveness, feasibility, and robustness.

Unmanned Surface Vehicle Thruster Fault Diagnosis via Vibration Signal Wavelet Transform and Vision Transformer under Varying Rotational Speed Conditions.

Among unmanned surface vehicle (USV) components, underwater thrusters are pivotal in their mission execution integrity. Yet, these thrusters directly interact with marine environments, making them perpetually susceptible to malfunctions. To diagnose thruster faults, a non-invasive and cost-effective vibration-based methodology that does not require altering existing systems is employed. However, the vibration data collected within the hull is influenced by propeller-fluid interactions, hull damping, and structural resonant frequencies, resulting in noise and unpredictability. Furthermore, to differentiate faults not only at fixed rotational speeds but also over the entire range of a thruster's rotational speeds, traditional frequency analysis based on the Fourier transform cannot be utilized. Hence, Continuous Wavelet Transform (CWT), known for attributions encapsulating physical characteristics in both time-frequency domain nuances, was applied to address these complications and transform vibration data into a scalogram. CWT results are diagnosed using a Vision Transformer (ViT) classifier known for its global context awareness in image processing. The effectiveness of this diagnosis approach was verified through experiments using a USV designed for field experiments. Seven cases with different fault types and severity were diagnosed and yielded average accuracy of 0.9855 and 0.9908 at different vibration points, respectively.

A GOA-Based Fault-Tolerant Trajectory Tracking Control for an Underwater Vehicle of Multi-Thruster System Without Actuator Saturation

This paper proposes an intelligent fault-tolerant control (FTC) strategy to tackle the trajectory tracking problem of an underwater vehicle (UV) under thruster damage (power loss) cases and meanwhile resolve the actuator saturation brought by the vehicle’s physical constraints. In the proposed control strategy, the trajectory tracking component is formed by a refined backstepping algorithm that controls the velocity variation and a sliding mode control deducts the torque/force outputs; the fault-tolerant component is established based on a Grasshopper Optimization Algorithm (GOA), which provides fast convergence speed as well as satisfactory accuracy of deducting optimized reallocation of the thruster forces to compensate for the power loss in different fault cases. Simulations with or without environmental perturbations under different fault cases and comparisons to other traditional FTCs are presented, thus verifying the effectiveness and robustness of the proposed GOA-based fault-tolerant trajectory tracking design. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the actuator saturation problem that exists in the trajectory tracking of an underwater vehicle (UV) when encountering power loss of the thruster system. The fault-tolerance trajectory tracking performance is affected by physical constraints of the vehicle when using the traditional methods as they may deduct excessive kinematic/dynamic requirements during the control process, thus inducing the deviation of the tracking trajectory. Therefore, the refined backstepping as well as the grasshopper optimization (GOA) are combined to eliminate the excess, where the refined backstepping is used to alleviate the speed jumps (kinematic outputs) and the GOA is to control the propulsion forces (dynamic outputs) when facing thruster fault cases. This innovates the industrial practitioners that the control design of the vehicle can be improved to avoid the tracking deviation brought by unsatisfied driving commands under fault cases through embedding optimization algorithms. Moreover, for the specific type of UV studied in this paper used for dam detection, simulations regarding practical dam detection such as the 3D polygonal line trajectory tracking and the frequently occurring UV single-fault cases are chosen, which can serve as references for practitioners working in the related field. In the future, underwater experiments of the UV will be investigated, with more effects of the practical environment involved.

Robust adaptive control of dynamic positioning ship under thruster faults and unknown disturbances

The robust adaptive anti-disturbance fault-tolerant control strategy for the dynamic positioning (DP) system of ships is built on the disturbance observer, the adaptive fault observer with the vectorial backstepping approach. The disturbance observer is constructed to estimate the first-order Markov disturbances. The adaptive fault observer with the projection algorithm is designed to estimate partial thruster faults. By employing the vectorial backstepping method, the robust adaptive anti-disturbance fault-tolerant control law is developed to simultaneously achieve the disturbance compensation and the partial thruster fault tolerant. It is demonstrated using the Lyapunov functions that the robust adaptive anti-disturbance fault-tolerant controller can maintain the DP of the ship’s position and heading to achieve the desired value, while guaranteeing the global stability of all signals in the DP closed-loop control system. Finally, different cases in the unknown ocean disturbance environment demonstrate the effectiveness of the robust adaptive anti-disturbance fault-tolerant control strategy.

Adaptive fault tolerant control scheme based on observer-controller integrated design for deep-sea vehicles

This paper investigates the adaptive fault tolerant control problem for deep-sea vehicles subject to thruster saturation, dead zone and measurement noise. In order to reduce the conservativeness of the fault tolerant control system, an integrated design of observer estimation and adaptive fault tolerant control is proposed for deep-sea vehicles based on Nussbaum function. Nussbaum functions are used to separate the compensation of the nonlinear factors such as actuator fault and dead zone in the observer and controller from the traditional generalized uncertainty term. Meanwhile, a diagonal matrix with parameters dynamically adjusted based on the tracking error norm is introduced into the control law. The combined stability analysis of the observer and controller is presented in this paper, and the separation principle of the observer and controller is avoided. Finally, the simulation results of the underwater vehicle verify that the proposed method can effectively reduce the energy consumption of the control signals while maintaining the tracking accuracy, and the largest yaw error under the serious thruster fault is reduced by 40% at least compared with other control schemes.

Design of UDE-based finite-time fault-tolerant control for DP vessels with complex disturbances and input constraints

This paper presents a novel uncertainty and disturbance estimator (UDE) – based finite-time fault-tolerant control scheme, applied to the controller design of dynamic positioning (DP) vessels with environmental disturbances, model uncertainty, input saturation, and thruster failures. The proposed method employs a non-singular terminal sliding surface to achieve finite-time stability based on the vessel's position and velocity errors. A UDE is to estimate environmental disturbances and model uncertainties. The auxiliary dynamic system (ADS) and smooth switching function are adopted to compensate for input constraints and avoid singularity caused by ADS. Further, a proposed fault observer is to estimate the thruster's fault state. In partial thruster failure, the utilization of the remaining execution ability of the failed thrusters to achieve vessel control objectives. Theoretical and simulation results show that the proposed method has a fast response, high control accuracy, strong disturbance resistance, and fault tolerance, making it superior to recent and classical trajectory tracking control.

Correlation analysis and fault detection of current and speed signals for underwater thruster entanglement

AbstractThe fault detection with a single sensor cannot comprehensively use the multi‐sensor correlation information of underwater thruster. To solve the issue that in the case of entanglement, a kind of fault diagnosis method of underwater thruster based on the combination of current and rotational speed signal correlation analysis and support vector machine is proposed. Firstly, the collected current and speed signals of underwater thrusters under different states are normalized, the variable sampling data points is adopted to refine the time of fault occurrence; Secondly, the cross correlation and autocorrelation coefficients of normalized current and speed signals are calculated based the variable sampling data points number and the correlation matrix is formed at different sampling time based on the cross correlation and autocorrelation coefficients. Finally, the support vector machine was applied to diagnose whether or not the fault produces and the time of fault occurrence with the correlation coefficients sequence. To verify the effectiveness of the proposed method, the fault simulation platform and data acquisition software are designed, the fault data in the case of thruster with entanglement are collected in time to test the proposed method. The results show that compared with the fault diagnosis method using only one speed or current signal, the proposed analytical method based on the correlation coefficients can fully extract the correlation information of underwater thruster fault, and the time extraction of fault occurrence is more sufficient, effectively improving the accuracy of underwater thruster fault diagnosis.

Fault-Tolerant Trajectory Tracking Based on Improved Chaotic Firefly Algorithm Model Prediction Control for Human-Occupied Vehicle With Ocean Current

In this article, aiming at the characteristics of model uncertainty and low-speed of human-occupied vehicle (HOV) system, a cascaded controller in ocean current environment is proposed. First, the kinematics controller is designed by improved chaotic firefly algorithm (ICFA)-model predictive control (MPC) to get the speed control signal. The multivariable constraint capability of ICFA-MPC can solve the problem of speed jump caused by thruster fault and further solve the problem of driving saturation. Second, the kinematic controller is combined with the dynamics controller which is according to the adaptive algorithm. The adjustable control parameters capability of the adaptive control can further obtain the dynamic control law under the influence of ocean current. Finally, the hybrid allocation strategy based on ICFA is used to redistribute the thruster force/moments. Simulation results displayed the dynamic trajectory tracking fault-tolerant controller can steadily track the reference trajectory under thruster fault without tracking disturbance, speed jump, and driving saturation problem.

Adaptive neural network projection analytical fault-tolerant control of underwater salvage robot with event trigger.

To solve the problem of control failure caused by system failure of deep-water salvage equipment under severe sea conditions, an event-triggered fault-tolerant control method (PEFC) based on proportional logarithmic projection analysis is proposed innovatively. First, taking the claw-type underwater salvage robot as the research object, amore universal thruster fault model was established to describe the fault state of equipment failure, interruption, stuck, and poor contact. Second, the controller was designed by the proportional logarithmic projection analytical method. The system input signal was amplified and projected as a virtual input, which replaces the original input to isolate and learn the fault factor online by the analytical algorithm. The terminal sliding mode observer was used to compensate for the external disturbance of the system, and the adaptive neural network was used to fit the dynamic uncertainty of the system. The system input was introduced into the event-triggered mechanism to reduce the output regulation frequency of the fault thruster. Finally, the simulation results showed that the method adopted in this study reduced the power output by 28.95% and the update frequency of power output by 75% compared with the traditional adaptive overdrive fault-tolerant control (AOFC) method and realized accurate pose tracking under external disturbance and system dynamic uncertain disturbance. It has been proven that the algorithm used in this research can still reasonably allocate power to reduce the load of a fault thruster and complete the tracking task under fault conditions.

Adaptive reinforcement learning fault-tolerant control for AUVs With thruster faults based on the integral extended state observer

In this paper, a reinforcement learning (RL) fault-tolerant control (FTC) method is proposed for trajectory tracking of autonomous underwater vehicles (AUVs) with thruster faults. To deal with the thruster fault, unknown disturbance and model uncertainty, a new integral extended state observer (IESO) for fault diagnosis observation is proposed, which uses a conventional ESO to estimate the total system uncertainty, and introduces an integral mechanism to mitigate the effect of estimation error further. Thus, the problem that the estimation error caused by the traditional ESO leads to the decline of the fault-tolerant capability of the FTC system is solved. Then, to solve the problem of integral saturation due to the introduction of the integral term, the integral term is limited after the thruster fault of the AUV. Furthermore, based on the actor–critic structure of RL, a PD-like feedback controller is designed to realize the FTC of AUV in the face of thruster fault by using the total uncertainty of the IESO scheme. And the input saturation of the thruster is considered, and an auxiliary variable system is used to handle the control truncation between saturated and unsaturated inputs. Based on the Lyapunov method, the stability of the closed-loop system is analyzed and proved. Finally, the proposed method is verified to have good fault tolerance and robustness by simulation and underwater experiments.

Optimization of Spacecraft Thrusters Configuration Under Fault Diagnosability and Recoverability Constraints

This paper investigates the problem of optimal placement (position and orientation) of spacecraft thrusters, under fault diagnosability and fault recoverability constraints. Avionics equipment contamination and plume impingement are also considered. The goal is to find the thrusters configuration with the minimal number of thrusters required for a given spacecraft architecture, so that it is guaranteed that it is possible to equip the control unit with a model-based fault diagnosis and fault-tolerant control solution, able to accommodate any single thruster's fault. This includes total loss of controllability of the faulty thruster. The proposed solution is a model-based solution in the sense that it is based on the spacecraft attitude and translational dynamics. With the help of the zonotope concept and its so-called H-representation, it is shown that this problem can be formulated as a nonlinear constrained optimization problem, which can be solved efficiently using hybrid optimization techniques. The proposed solution is assessed on a generic spacecraft architecture that performs a proximity maneuver.

A Transferable Thruster Fault Diagnosis Approach for Autonomous Underwater Vehicle under Different Working Conditions with Insufficient Labeled Training Data

Existing thruster fault diagnosis methods for AUV (autonomous underwater vehicle) usually need sufficient labeled training data. However, it is unrealistic to get sufficient labeled training data for each working condition in practice. Based on this challenge, a transferable thruster fault diagnosis approach is proposed. In the approach, an IPSE (instantaneous power spectrum entropy) and a STNED (signal-to-noise energy difference) are added to SPWVD (smoothed pseudo Wigner-Ville distribution) to identify time and frequency boundaries of the local region in the time-frequency power spectrum caused by thruster fault, forming a TFE (time-frequency energy) method for feature extraction. In addition, the RCQFFV (relative change quantity of the fault feature value), an MSN (multiple scale normalization) and a LSP (least square prediction) are added to SVDD (support vector data description) to align distributions of fault samples, contributing a TSVDD (transferable SVDD) for classification of fault samples. The experimental results of a prototype AUV indicate that the fault feature is monotonic to the percentage of thrust loss for the proposed TFE but not for the SPWVD. The TSVDD has a higher overall classification accuracy in comparison to conventional SVDD under working conditions with no labeled training data.

Thruster fault identification using improved peak region energy and multiple model least square support vector data description for autonomous underwater vehicle

This article investigates a novel fault identification approach to determine the percentage of the thrust loss for autonomous underwater vehicle thrusters. The novel approach is developed from a combination of the peak region energy (PRE) and support vector data description (SVDD) by considering that PRE is able to acquire a primary feature in low dimensions from signals without any secondary process and that SVDD can establish a hypersphere boundary for a class of fault samples even in the case of a small number of training samples. Three improvements, namely removing the fusion, an energy leakage and a homomorphic transform are applied to the PRE. It forms an improved PRE to increase the area under the curve. Furthermore, another three new contents, namely the least square, a multiple model fusion and a dead zone are added to the SVDD. It constructs a multiple model least square SVDD to increase the overall identification accuracy. Experiments are performed on an experimental prototype autonomous underwater vehicle in a pool. The experimental results indicate the effectiveness of the proposed method.

Fault Diagnosis Method for an Underwater Thruster, Based on Load Feature Extraction

Targeting the problem of fault diagnosis in magnetic coupling underwater thrusters, a fault pattern classification method based on load feature extraction is proposed in this paper. By analyzing the output load characteristics of thrusters under typical fault patterns, the load torque model of the thrusters is established, and two characteristic parameters are constructed to describe the different fault patterns of thrusters. Then, a thruster load torque reconstruction method, based on the sliding mode observer (SMO), and the fault characteristic parameter identification method, based on the least square method (LSM), are proposed. According to the identified fault characteristic parameters, a thruster fault pattern classification method based on a support vector machine (SVM) is proposed. Finally, the feasibility and superiority of the proposed aspects are verified, through comparative simulation experiments. The results show that the diagnostic accuracy of this method is higher than 95% within 5 seconds of the thruster fault. The lowest diagnostic accuracy of thrusters with a single failure state is 96.75%, and the average diagnostic accuracy of thrusters with five fault states is 98.65%.

Finite-Time Extended State Observe Based Fault Tolerant Control for Autonomous Underwater Vehicle with Unknown Thruster Fault

This paper investigates the problem of fault tolerant control (FTC) for autonomous underwater vehicles (AUVs) with multiple thrusters in the presence of current disturbances, thruster faults, and modelling uncertainty. This paper focuses on the problems of reducing the energy consumption caused by the chattering of control signals and improving the tracking accuracy of an AUV operating in deep-sea environments. In view of the problem of large energy consumption in some other methods, a fault tolerant control method for multiple-thruster AUVs based on a finite-time extended state observer (FTESO) is proposed. More specifically, a FTESO based on an integral sliding mode surface is designed to estimate the generalized uncertainty compounded using current disturbances, thruster faults, and modelling uncertainty. The fast finite-time uniformly ultimately bounded stability of the proposed FTESO is analyzed. Then, based on the estimated value of FTESO, an FTC method based on non-singular fast terminal sliding mode surfaces is developed for AUVs. The finite-time convergence of the closed-loop control system is proved theoretically. In this design, two different sliding mode surfaces are used to design FTESO and FTC, in order to avoid the appearance of singularities. Moreover, a parameter adjustment method is designed to improve tracking accuracy. Finally, comparative numerical simulations show that the proposed control scheme is effective at reducing energy consumption and improving tracking accuracy.

Adaptive neural networks trajectory tracking control for autonomous underwater helicopters with prescribed performance

An autonomous underwater helicopter (AUH) is a type of autonomous underwater vehicle (AUV) that is capable of fixed-point hovering, precise and free take-off, and landing. It is effective for underwater ultra-mobile tasks, including mobile observation networks, resource exploration, and data connection. This paper aims to investigate an AUH trajectory tracking controller based on the prescribed performance method, considering the influence factors such as current disturbance, modeling uncertainty and thruster faults. A novel preset time performance function is designed in which the stable terminal time of the system can be specified explicitly, and the convergence rate of the system's dynamic process may be altered by adjusting the parameters, making it more intuitive for the designer. A speed observer was introduced to address this problem of measuring the required state information from the AUH's sensors in practice, which was enhanced using a radial basis function neural network (RBFNN) to approximate external perturbations and uncertainties. The stability of the closed-loop AUH system is proved by using Lyapunov theory. The feasibility and effectiveness of the proposed algorithm proposed approach were eventually verified by two sets of simulations for different thruster fault forms.

Quantized Output-Feedback Control for Unmanned Marine Vehicles With Thruster Faults via Sliding-Mode Technique.

This article is concerned with the quantized output-feedback control problem for unmanned marine vehicles (UMVs) with thruster faults and ocean environment disturbances via a sliding-mode technique. First, based on output information and compensator states, an augmented sliding surface is constructed and sliding-mode stability through linear matrix inequalities can be guaranteed. An improved quantization parameter dynamic adjustment scheme, with a larger quantization parameter adjustment range, is then given to compensate for quantization errors effectively. Combining the quantization parameter adjustment strategy and adaptive mechanism, a novel robust sliding-mode controller is designed to guarantee the asymptotic stability of a closed-loop UMV system. As a result, a smaller lower bound of the thruster fault factor than that of the existing result can be tolerated, which brings more practical applications. Finally, the comparison simulation results have illustrated the effectiveness of the proposed method.

Design of Underwater Thruster Fault Detection Model Based on Vibration Sensor Data: Generative Adversarial Network-based Fault Data Expansion Approach for Data Imbalance

Design of Underwater Thruster Fault Detection Model Based on Vibration Sensor Data: Generative Adversarial Network-based Fault Data Expansion Approach for Data Imbalance

Thruster Fault Diagnostics and Fault Tolerant Control for Autonomous Underwater Vehicle with Ocean Currents

Autonomous underwater vehicle (AUV) is one of the most important exploration tools in the ocean underwater environment, whose movement is realized by the underwater thrusters, however, the thruster fault happens frequently in engineering practice. Ocean currents perturbations could produce noise for thruster fault diagnosis, in order to solve the thruster fault diagnostics, a possibilistic fuzzy C-means (PFCM) algorithm is proposed to realize the fault classification in this paper. On the basis of the results of fault diagnostics, a fuzzy control strategy is proposed to solve the fault tolerant control for AUV. Considering the uncertainty of ocean currents, it proposes a min-max robust optimization problem to optimize the fuzzy controller, which is solved by a cooperative particle swarm optimization (CPSO) algorithm. Simulation and underwater experiments are used to verify the accuracy and feasibility of the proposed method of thruster fault diagnostics and fault tolerant control.

무인 수상선의 수중 추진기 고장 진단 연구를 위한 고장 특성 인자 선정 및 실험

As interest in unmanned vessel research has increased, emphasis on autonomous navigation capability and the sustainability of normal operation has been highlighted. However, research on fault detection and diagnosis (FDD) for unmanned surface vehicles (USVs) is insufficient compared with research on land machinery or unmanned aerial vehicles. Among prior studies on USV FDD, intensive analysis or diagnostics for individual elements with high failure probability were barely conducted. This study focus on fault detecting features of a USV thruster, one of the most failure-prone elements other than internal causes or software malfunctions. A hypothesis for thruster fault detecting features was made based on general thruster-related equations; the features were tested on a stand-alone thruster test to assess the feature’s acceptability. However, a stand-alone thruster test lacks important effects such as structural vibratory influence of an actual USV, thruster load difference due to inflow velocity and wave-induced vibrations as the USV slides on the water’s surface. Hence, USV capable of acquiring fine quality real-time data for FDD practice was developed, and a surface driving test in a water basin was conducted to confirm the eligibility of the selected features.