An Aircraft Upset Recovery System with Reinforcement Learning

• URL: http://arxiv.org/abs/2604.24355v1
• Date: Mon, 27 Apr 2026 11:48:19 GMT
• Title: An Aircraft Upset Recovery System with Reinforcement Learning
• Abstract summary: This article explores the progress made in the creation of a pilot activated recovery system (PARS) for advanced jet trainers.<n>The PARS model employs an advanced reinforcement learning (RL) architecture, incorporating a cutting-edge soft-actor critic (SAC) model.<n>Negative-g punishments and other handcrafted features remarked upon by control engineers and domain experts regarding PARS are also taken into account by the system.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article explores the progress made in the creation of a pilot activated recovery system (PARS) for advanced jet trainers that utilizes artificial intelligence (AI) in an effort to enhance operational efficiency. The PARS model employs an advanced reinforcement learning (RL) architecture, incorporating a cutting-edge soft-actor critic (SAC) model and hyper-parameter optimization methods. Negative-g punishments and other handcrafted features remarked upon by control engineers and domain experts regarding PARS are also taken into account by the system. When evaluated by them, the AI model's behavior is deemed more desirable than that of conventional control methods.

Related papers

• An Automatic Ground Collision Avoidance System with Reinforcement Learning [0.0]
  This article evaluates an artificial intelligence (AI)-based Automatic Ground Collision Avoidance System (AGCAS) designed for advanced jet trainers to enhance operational effectiveness.<n>The system utilizes line-of-sight queries on a terrain server to ensure precise and efficient collision avoidance.
  arXiv  Detail & Related papers  (2026-04-27T12:27:08Z)
• Cost-effective Reduced-Order Modeling via Bayesian Active Learning [12.256032958843065]
  We propose BayPOD-AL, an active learning framework based on an uncertainty-aware Bayesian proper decomposition (POD) approach.<n> Experimental results on predicting the temperature evolution over a rod demonstrate BayPOD-AL's effectiveness in suggesting the informative data.<n>We demonstrate BayPOD-AL's generalizability and efficiency by evaluating its performance on a dataset of higher temporal resolution than the training dataset.
  arXiv  Detail & Related papers  (2025-06-27T21:23:37Z)
• Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models [57.582219834039506]
  We introduce the training methodologies implemented in the development of Skywork-MoE, a high-performance mixture-of-experts (MoE) large language model (LLM) with 146 billion parameters and 16 experts. It is based on the pre-existing dense checkpoints of our Skywork-13B model.
  arXiv  Detail & Related papers  (2024-06-03T03:58:41Z)
• Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents [49.85633804913796]
  We present an exploration-based trajectory optimization approach, referred to as ETO. This learning method is designed to enhance the performance of open LLM agents. Our experiments on three complex tasks demonstrate that ETO consistently surpasses baseline performance by a large margin.
  arXiv  Detail & Related papers  (2024-03-04T21:50:29Z)
• Modelling, Positioning, and Deep Reinforcement Learning Path Tracking Control of Scaled Robotic Vehicles: Design and Experimental Validation [3.807917169053206]
  Scaled robotic cars are commonly equipped with a hierarchical control acthiecture that includes tasks dedicated to vehicle state estimation and control. This paper covers both aspects by proposing (i) a federeted extended Kalman filter (FEKF) and (ii) a novel deep reinforcement learning (DRL) path tracking controller trained via an expert demonstrator. The experimentally validated model is used for (i) supporting the design of the FEKF and (ii) serving as a digital twin for training the proposed DRL-based path tracking algorithm.
  arXiv  Detail & Related papers  (2024-01-10T14:40:53Z)
• TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework [58.474610046294856]
  Industrial systems demand reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. This paper introduces an integrated framework that leverages the capabilities of the Transformer model-based neural networks and deep reinforcement learning (DRL) algorithms to optimize system maintenance actions.
  arXiv  Detail & Related papers  (2023-09-29T02:27:54Z)
• REX: Rapid Exploration and eXploitation for AI Agents [103.68453326880456]
  We propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX. REX introduces an additional layer of rewards and integrates concepts similar to Upper Confidence Bound (UCB) scores, leading to more robust and efficient AI agent performance.
  arXiv  Detail & Related papers  (2023-07-18T04:26:33Z)
• Constrained Reinforcement Learning for Robotics via Scenario-Based Programming [64.07167316957533]
  It is crucial to optimize the performance of DRL-based agents while providing guarantees about their behavior. This paper presents a novel technique for incorporating domain-expert knowledge into a constrained DRL training loop. Our experiments demonstrate that using our approach to leverage expert knowledge dramatically improves the safety and the performance of the agent.
  arXiv  Detail & Related papers  (2022-06-20T07:19:38Z)
• ReIL: A Framework for Reinforced Intervention-based Imitation Learning [3.0846824529023387]
  We introduce Reinforced Intervention-based Learning (ReIL), a framework consisting of a general intervention-based learning algorithm and a multi-task imitation learning model. Experimental results from real world mobile robot navigation challenges indicate that ReIL learns rapidly from sparse supervisor corrections without suffering deterioration in performance.
  arXiv  Detail & Related papers  (2022-03-29T09:30:26Z)
• RL-Controller: a reinforcement learning framework for active structural control [0.0]
  We present a novel RL-based approach for designing active controllers by introducing RL-Controller, a flexible and scalable simulation environment. We show that the proposed framework is easily trainable for a five story benchmark building with 65% reductions on average in inter story drifts. In a comparative study with LQG active control method, we demonstrate that the proposed model-free algorithm learns more optimal actuator forcing strategies.
  arXiv  Detail & Related papers  (2021-03-13T04:42:13Z)
• Boosting Adversarial Training with Hypersphere Embedding [53.75693100495097]
  Adversarial training is one of the most effective defenses against adversarial attacks for deep learning models. In this work, we advocate incorporating the hypersphere embedding mechanism into the AT procedure. We validate our methods under a wide range of adversarial attacks on the CIFAR-10 and ImageNet datasets.
  arXiv  Detail & Related papers  (2020-02-20T08:42:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.