Switching Control Architecture with Parametric Optimization for Aircraft Upset Recovery

Yildiz, Anil; Akcal, M. Ugur; Hostas, Batuhan; Üre, Nazım Kemal

doi:10.2514/1.g004180

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…The actions directly affect the training effect of strategies. To quickly change the aircraft state at the initial stage of upset recovery and precisely adjust the aircraft state at the later stage of upset recovery, the action adopted in this study was the deflection change value of the control surface, as shown in Equation (13), where the number of actions was 27, and the thrust was set to 0 to prevent speed exceeding the limit or height loss [17].…”

Section: Actionsmentioning

confidence: 99%

“…Dongmo [12] proposed an LOC recovery scheme that combines feedback linearization and high-order sliding mode control. Yildiz [13] proposed a switching control architecture with parametric optimization for aircraft upset recovery. Richards [14] proposed an upset recovery architecture that is applicable to both piloted and autonomous recoveries.…”

Section: Introductionmentioning

confidence: 99%

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Aircraft Upset Recovery Strategy and Pilot Assistance System Based on Reinforcement Learning

Wang,

Zhao,

Zhang

et al. 2024

Aerospace

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The upset state is an unexpected flight state, which is characterized by an unintentional deviation from normal operating parameters. It is difficult for the pilot to recover the aircraft from the upset state accurately and quickly. In this paper, an upset recovery strategy and pilot assistance system (PAS) based on reinforcement learning is proposed. The man–machine closed-loop system was established and the upset state, such as a high angle of attack and large attitude angle, was induced. The upset recovery problem was transformed into a sequential decision problem, and the Markov decision model of upset recovery was established by taking the deflection change of the control surface as the action. The proximal policy optimization (PPO) algorithm was selected for the strategy training. The adaptive pilot model and the reinforcement learning method proposed in this paper were used to make the aircraft recover from the upset state. Based on the correspondence between the flight state, the recovery method, and the recovery result, the aircraft upset recovery safety envelopes were formed, and the four-level upset recovery PAS with alarm warning, coordinated control, and autonomous recovery modes was constructed. The results of the digital virtual flight simulation and ground flight test show that compared with a traditional single pilot, the aircraft upset recovery strategy, the upset recovery safety envelopes, and the PAS established in this study could reduce the handling burden of the pilot and improve the success rate and effect of upset recovery. This research has certain theoretical reference values for flight safety and pilot training.

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Section: Actionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aircraft Upset Recovery Strategy and Pilot Assistance System Based on Reinforcement Learning

Wang,

Zhao,

Zhang

et al. 2024

Aerospace

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“…Therefore, ICAO lists the prevention of LOC-I as one of its top safety priorities [1]. In fact, the vast majority of LOC-I accidents are caused by airplane upsets [11]. Therefore, it is very important to study the risk of airplane upsets and explore the risk evolution mechanism for improving flight safety.…”

Section: Introductionmentioning

confidence: 99%

Risk Analysis of Airplane Upsets in Flight: An Integrated System Framework and Analysis Methodology

Meng

2023

Aerospace

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Generally, airplane upsets in flight are considered a precursor to loss of control in flight (LOC-I) accidents, and unfortunately LOC-I is classified as the leading cause of fatal accidents. To further explore the risk factors, causal relationships, and coupling mechanism of airplane upsets, this study proposed a risk analysis model integrating the Interpretative Structural Modeling (ISM) and Bayesian Network (BN). Seventeen key risk factors leading to airplane upsets were identified through the analysis of typical accident cases and the literature. The ISM approach was used to construct the multi-level interpretative structural model of airplane upsets, which could reveal the causal relationship among various risk factors and risk propagation paths. Then, taking 286 accident/incident investigation data as training samples, a data-driven BN model was established using machine learning for dependency intensity assessment and inference analysis. The results reveal that the interaction among risk factors of fatal accidents caused by airplane upsets is more significant than that of non-fatal accidents/incidents. Risk factors such as pilot-induced oscillations/airplane-pilot coupling and non-adherence to Standard Operating Procedures (SOPs)/neglect of cross-validation have a significant effect on airplane upsets in flight among seventeen risk factors. Moreover, this study also identifies the most likely set of risk factors that lead to fatal accidents caused by airplane upsets. The research results have an important theoretical significance and application value for preventing airplane upsets risk.

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Two-Stage Strategy to Achieve a Reinforcement Learning-Based Upset Recovery Policy for Aircraft

Cao

Zeng

Jiang

et al. 2021

2021 China Automation Congress (CAC)

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Switching Control Architecture with Parametric Optimization for Aircraft Upset Recovery

Cited by 8 publications

References 28 publications

Aircraft Upset Recovery Strategy and Pilot Assistance System Based on Reinforcement Learning

Aircraft Upset Recovery Strategy and Pilot Assistance System Based on Reinforcement Learning

Risk Analysis of Airplane Upsets in Flight: An Integrated System Framework and Analysis Methodology

Two-Stage Strategy to Achieve a Reinforcement Learning-Based Upset Recovery Policy for Aircraft

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