Identification and Optimal Nonlinear Control of Turbojet Engine Using Koopman Eigenfunction Model

• URL: http://arxiv.org/abs/2505.10438v2
• Date: Thu, 29 May 2025 13:02:34 GMT
• Title: Identification and Optimal Nonlinear Control of Turbojet Engine Using Koopman Eigenfunction Model
• Authors: David Grasev,
• Abstract summary: The rotor dynamics were estimated using the sparse identification of nonlinear dynamics.<n>The resulting Koopman model was validated against an in-house reference component-level model.<n>The eigenmode structure allowed targeting individual modes during the optimization process, resulting in a better performance tuning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Gas turbine engines represent complex highly nonlinear dynamical systems. Deriving their physics-based models can be challenging as it requires performance characteristics, that are not always available, and one often has to make many simplifying assumptions. In this paper, the limitations of conventional experimental methods used to derive component-level and locally linear parameter-varying models are discussed and addressed by employing identification techniques based on data collected from standard engine operation under closed-loop control. The rotor dynamics were estimated using the sparse identification of nonlinear dynamics. Subsequently, the autonomous part of the dynamics was mapped into an optimally constructed Koopman eigenfunction space. The process included eigenvalue optimization using metaheuristic algorithms and temporal projection, followed by gradient-based eigenfunction identification. The resulting Koopman model was validated against an in-house reference component-level model. A globally optimal nonlinear feedback controller and a Kalman estimator were then designed in the eigenfunction space and compared to the classical and gain-scheduled proportional-integral controllers, as well as a proposed internal model control approach. The eigenmode structure allowed targeting individual modes during the optimization process, resulting in a better performance tuning. The results showed that the Koopman-based controller outperformed the other benchmark controllers in both reference tracking and disturbance rejection, under sea-level and varying flight conditions, due to its global nature.

Related papers

• Deep Bilinear Koopman Model for Real-Time Vehicle Control in Frenet Frame [0.0]
  This paper presents a deep Koopman approach for modeling and control of vehicle dynamics within the curvilinear Frenet frame.<n>The proposed framework uses a deep neural network architecture to simultaneously learn the Koopman operator and its associated invariant subspace from the data.<n>The proposed controller achieved significant reductions in tracking error relative to baseline controllers, confirming its suitability for real-time implementation in embedded autonomous vehicle systems.
  arXiv  Detail & Related papers  (2025-07-16T18:49:44Z)
• Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems [49.819436680336786]
  We propose an efficient transformed Gaussian process state-space model (ETGPSSM) for scalable and flexible modeling of high-dimensional, non-stationary dynamical systems.<n>Specifically, our ETGPSSM integrates a single shared GP with input-dependent normalizing flows, yielding an expressive implicit process prior that captures complex, non-stationary transition dynamics.<n>Our ETGPSSM outperforms existing GPSSMs and neural network-based SSMs in terms of computational efficiency and accuracy.
  arXiv  Detail & Related papers  (2025-03-24T03:19:45Z)
• Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection [4.248022697109535]
  We use a Koopman-inspired architecture called the Linear Recurrent Autoencoder Network (LRAN) for learning reduced-order dynamics in convection flows. A traditional fluid dynamics method, the Kernel Dynamic Mode Decomposition (KDMD) is used to compare the LRAN. We obtained more accurate predictions with the LRAN than with KDMD in the most turbulent setting.
  arXiv  Detail & Related papers  (2024-05-10T12:15:02Z)
• Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study [56.283944756315066]
  We propose generic model structures combining delay-coordinate encoding of measurements and full-state decoding to integrate reduced Koopman modeling and state estimation. A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of a high-purity cryogenic distillation column.
  arXiv  Detail & Related papers  (2024-01-09T11:54:54Z)
• Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control [46.81433026280051]
  We present a self-supervised learning approach that actively models the dynamics of nonlinear robotic systems. Our approach showcases high resilience and generalization capabilities by consistently adapting to unseen flight conditions.
  arXiv  Detail & Related papers  (2022-10-23T00:45:05Z)
• Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories [1.534667887016089]
  We write the dynamics of observables governed by the Koopman generator as a bilinear hidden Markov model. We demonstrate the performance of this method on three examples.
  arXiv  Detail & Related papers  (2022-09-20T20:10:03Z)
• Adaptive LASSO estimation for functional hidden dynamic geostatistical model [69.10717733870575]
  We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hiddenstatistical models (f-HD) The algorithm is based on iterative optimisation and uses an adaptive least absolute shrinkage and selector operator (GMSOLAS) penalty function, wherein the weights are obtained by the unpenalised f-HD maximum-likelihood estimators.
  arXiv  Detail & Related papers  (2022-08-10T19:17:45Z)
• 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)
• Bayesian optimization of distributed neurodynamical controller models for spatial navigation [1.9249287163937971]
  We introduce the NeuroSwarms controller, in which agent-based interactions are modeled by analogy to neuronal network interactions. This complexity precludes linear analyses of stability, controllability, and performance typically used to study conventional swarm models. We present a framework for tuning dynamical controller models of autonomous multi-agent systems based on Bayesian Optimization.
  arXiv  Detail & Related papers  (2021-10-31T21:43:06Z)
• Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization [60.73540999409032]
  We show that expressive autoregressive dynamics models generate different dimensions of the next state and reward sequentially conditioned on previous dimensions. We also show that autoregressive dynamics models are useful for offline policy optimization by serving as a way to enrich the replay buffer.
  arXiv  Detail & Related papers  (2021-04-28T16:48:44Z)
• Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
  We propose a novel compound kernel that captures the control-affine nature of the problem. We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
  arXiv  Detail & Related papers  (2020-11-14T01:27:32Z)
• Derivative-Based Koopman Operators for Real-Time Control of Robotic Systems [14.211417879279075]
  This paper presents a generalizable methodology for data-driven identification of nonlinear dynamics that bounds the model error. We construct a Koopman operator-based linear representation and utilize Taylor series accuracy analysis to derive an error bound. When combined with control, the Koopman representation of the nonlinear system has marginally better performance than competing nonlinear modeling methods.
  arXiv  Detail & Related papers  (2020-10-12T15:15:13Z)

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.