Fault-tolerant attitude tracking control for carrier-based aircraft using RBFNN-based adaptive second-order sliding mode control

Xiao, Huiyunuo; Zhen, Ziyang; Xue, Yixuan

doi:10.1016/j.ast.2023.108408

Cited by 14 publications

(8 citation statements)

References 43 publications

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“…Based on the aforementioned data, Equations (15) to (20) are used to conduct grey correlation analysis on the collected data in order to mitigate issues such as lagged variable introduction and indicator prediction failure caused by multicollinearity [65].…”

Section: Data Collection and Pre-processingmentioning

confidence: 99%

“…The indexes are weighted according to the weights obtained from Equations ( 14)~ (20) to obtain the financial stress indices of the money market (FSI Money), bond market (FSI Bond), stock market (FSI Stock), foreign exchange market (FSI Exchange), trade credit market (FSI Trade Credit) and external debt market (FSI External Debt), respectively. The financial stress indexes of six sub-markets, namely FSI Trade Credit, FSI External Debt, and FSI China, calculated according to Equation (7), are shown in Figure 4.…”

Section: Trend Analysis Of Fsismentioning

confidence: 99%

“…Additionally, in the pursuit of a dependable FSI prediction methodology, we have chosen to employ the Radial Basis Function Neural Network (RBFNN), a machine learning algorithm renowned for its robust capacity in nonlinear modeling. The proposed algorithm effectively interprets volatile data in financial sequences and demonstrates robust adaptability [20]. To address the challenges associated with parameter selection in the RBFNN, we propose an Improved Gravitational Search Algorithm (IGSA).…”

Section: Introductionmentioning

confidence: 99%

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Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

Tian,

2024

Mathematics

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Accurate measurement of systemic financial risk is crucial for maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was constructed by integrating six dimensions and employing Gray Relation Analysis (GRA) to reduce the dimensionality of the indicators. The CFSI was derived using the Attribute Hierarchy Model (AHM) method with the Criteria Importance Through the Intercriteria Correlation (CRITIC) method, and an Improved Gravitational Search Algorithm (IGSA)-optimized Radial Basis Function Neural Network (RBFNN) was proposed for out-of-sample prediction of CFSI trends from 2024 to 2026. By analyzing the trends in financial pressure indicators, the intricate relationship between financial pressure and economic activity can be effectively discerned. The research findings indicate that (1) the CFSI is capable of accurately reflecting the current financial stress situation in China, and (2) the IGSA-RBFNN demonstrates strong robustness and generalization capabilities, predicting that the CFSI index will reach a peak value of 0.543 by the end of 2024, and there exists a regular pattern of stress rebound towards the end of each year. The novel methodology enables policymakers and regulatory authorities to proactively identify potential risks and vulnerabilities, facilitating the formulation of preventive measures.

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Section: Data Collection and Pre-processingmentioning

confidence: 99%

Section: Trend Analysis Of Fsismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

Tian,

2024

Mathematics

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“…Various factors can induce actuator faults during CBA landing, also leading to landing failure. Common actuator faults are jamming, random drift, partial loss and saturation, and herein, the focus is mainly on partial loss [39], also known as efficiency loss. If this fault appears, then the actuator control directives experience deviations, leading to drops in gains, which further affect and weaken the actuator functions.…”

Section: Models Of Faults and Disturbancesmentioning

confidence: 99%

Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method

Xing,

2023

Applied Sciences

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Carrier-based aircraft landing involves complex system engineering characterised by strong nonlinearity, significant coupling and susceptibility to environmental disturbances, and autonomous landing of carrier-based aircraft under fault states is even more challenging and riskier. To address the control-system problems of loss of efficiency and performance due to actuator faults and performance degradation due to various unknown disturbances, presented here is fault-tolerant control for carrier-based aircraft based on adaptive fuzzy sliding-mode fault-tolerant control (AFSMFTC). First, three models are built (the carrier-based aircraft fault model, the carrier air wake model and the deck motion model), and the control framework of the autonomous landing control system is introduced. Next, a longitudinal and lateral flight channel controller comprising an adaptive fuzzy network, adaptive laws and a sliding-mode controller is designed using the AFSMFTC method. The adaptive fuzzy network implements fuzzy approximation for the sliding-mode switching terms to further offset errors induced by unknown disturbances, the adaptive laws compensate for actuator faults, and the sliding-mode controller ensures tracking of the overall flight path. Furthermore, the stability of the fault-tolerant method is demonstrated using the Lyapunov function. Finally, simulation and comparative experiments show that the proposed fault-tolerant method has outstanding control performance and strong fault-tolerant capability, thereby providing an effective and feasible solution for designing an autonomous landing system for carrier-based aircraft under fault states.

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“…Many scholars have studied the control of the carrier-based UAV. Reference [36] utilizes RBFNN to estimate the perturbations in the attitude control system of a carrier-based aircraft and designs a second-order sliding mode controller to ensure the stability of the attitude control system within a finite time. Reference [37] addresses the resource allocation problem for the UAV.…”

Section: Introductionmentioning

confidence: 99%

Prescribed Performance Fault-Tolerant Attitude Tracking Control for UAV with Actuator Faults

Wu,

Zhu

2024

Drones

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This paper proposes a prescribed performance fault-tolerant control based on a fixed-time extended state observer (FXTESO) for a carrier-based unmanned aerial vehicle (UAV). First, the attitude motion model of the UAV is introduced. Secondly, the proposed FXTESO is designed to estimate the total disturbances including coupling, actuator faults and external disturbances. By using the barrier Lyapunov function (BLF), it is proved that under prescribed performance control (PPC), the attitude tracking error is stable within the prescribed range. The simulation results for tracking the desired attitude angle show that the average overshoot and stabilization time of PPC-FXTESO is 0.00455rad and 6.2s. Comparatively, the average overshoots of BSC-ESO and BSC-FTESO are 0.035rad and 0.027rad, with stabilization times of 14.97s and 12.56s, respectively. Therefore, the control scheme proposed in this paper outperforms other control schemes.

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Fault-tolerant attitude tracking control for carrier-based aircraft using RBFNN-based adaptive second-order sliding mode control

Cited by 14 publications

References 43 publications

Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method

Prescribed Performance Fault-Tolerant Attitude Tracking Control for UAV with Actuator Faults

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