Learning based Modelling of Throttleable Engine Dynamics for Lunar Landing Mission

• URL: http://arxiv.org/abs/2511.08612v1
• Date: Thu, 13 Nov 2025 01:00:40 GMT
• Title: Learning based Modelling of Throttleable Engine Dynamics for Lunar Landing Mission
• Authors: Suraj Kumar, Aditya Rallapalli, Bharat Kumar GVP,
• Abstract summary: This paper presents a learning-based system identification approach for modelling of throttleable engine dynamics.<n>The developed model is validated with experimental results and used for closed-loop guidance and control simulations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Typical lunar landing missions involve multiple phases of braking to achieve soft-landing. The propulsion system configuration for these missions consists of throttleable engines. This configuration involves complex interconnected hydraulic, mechanical, and pneumatic components each exhibiting non-linear dynamic characteristics. Accurate modelling of the propulsion dynamics is essential for analyzing closed-loop guidance and control schemes during descent. This paper presents a learning-based system identification approach for modelling of throttleable engine dynamics using data obtained from high-fidelity propulsion model. The developed model is validated with experimental results and used for closed-loop guidance and control simulations.

Related papers

• Learning Quadrupedal Locomotion for a Heavy Hydraulic Robot Using an Actuator Model [7.275864116517311]
  This work is the first demonstration of a successful transfer of stable and robust command-tracking locomotion with reinforcement learning (RL) on a heavy hydraulic quadruped robot.<n>The model predicts joint torques for all 12 actuators in under 1 microsecond, allowing rapid processing in RL environments.
  arXiv  Detail & Related papers  (2026-01-16T10:01:09Z)
• Online learning of subgrid-scale models for quasi-geostrophic turbulence in planetary interiors [41.99844472131922]
  We study quasi-geostrophic turbulent flow in an axisymmetric bounded domain.<n>Flow is driven by a prescribed analytical forcing.<n>We show that an SGS model trained on data spanning only one turnover time remains stable and accurate over integrations at least a hundred times longer than the training period.
  arXiv  Detail & Related papers  (2025-11-18T15:21:38Z)
• Multi-fidelity Reinforcement Learning Control for Complex Dynamical Systems [42.2790464348673]
  We propose a multi-fidelity reinforcement learning framework for controlling instabilities in complex systems.<n>The effect of the proposed framework is demonstrated on two complex dynamics in physics.
  arXiv  Detail & Related papers  (2025-04-08T00:50:15Z)
• A Supervised Machine-Learning Approach For Turboshaft Engine Dynamic Modeling Under Real Flight Conditions [0.0]
  In this work, we explore different Neural Network architectures to model the turboshaft engine of Leonardo's AW189P4 prototype.<n>The models are trained on an extensive database of real flight tests featuring a variety of operational maneuvers.<n>To complement the neural network approach, we apply Sparse Identification of Dynamics (SINDy) to derive a low-dimensional dynamical model.
  arXiv  Detail & Related papers  (2025-02-19T21:45:53Z)
• A Deep Inverse-Mapping Model for a Flapping Robotic Wing [0.0]
  In systems control, the dynamics of a system are governed by its inputs to achieve a desired outcome.<n>In flapping-wing robots, intricate fluid motions are involved, mapping inputs (wing kinematics) to outcomes (aerodynamic forces) is nontrivial.<n>Here, we report a machine-learning solution for the inverse mapping of a flapping-wing system based on data from an experimental system we have developed.
  arXiv  Detail & Related papers  (2025-02-13T14:46:04Z)
• Probing Multimodal LLMs as World Models for Driving [72.18727651074563]
  We look at the application of Multimodal Large Language Models (MLLMs) in autonomous driving. Despite advances in models like GPT-4o, their performance in complex driving environments remains largely unexplored.
  arXiv  Detail & Related papers  (2024-05-09T17:52:42Z)
• A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential [71.53284767149685]
  We present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar. We include a micro-steps discretization approach to accurately linearize the dynamics and produce a prediction suitable for real-time implementation.
  arXiv  Detail & Related papers  (2023-12-22T16:29:55Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control [16.88250694156719]
  We present a novel hybrid model formulation that enables us to identify fully physically consistent inertial parameters of a rigid body dynamics model. We compare our approach against state-of-the-art inverse dynamics models on a 7 degree of freedom manipulator.
  arXiv  Detail & Related papers  (2022-05-27T07:39:28Z)
• Gradient-Based Trajectory Optimization With Learned Dynamics [80.41791191022139]
  We use machine learning techniques to learn a differentiable dynamics model of the system from data. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car.
  arXiv  Detail & Related papers  (2022-04-09T22:07:34Z)
• Identification of the nonlinear steering dynamics of an autonomous vehicle [0.0]
  Modern vehicles have a wide array of digital and mechatronic components that are difficult to model. It is attractive to use data-driven modelling to capture the relevant vehicle dynamics. We show that a neural network based subspace-encoder can successfully capture the underlying dynamics.
  arXiv  Detail & Related papers  (2021-05-10T17:32:23Z)
• Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction [75.1213178617367]
  We propose a hybrid approach combining deep learning and physical motion models. We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model. The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
  arXiv  Detail & Related papers  (2021-03-11T15:21:08Z)

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.