arXiv subjects

E. Javier Olucha

Publications and source records attributed to E. Javier Olucha.

7 recordsLinked to original sources

arXiv2026

Data-driven Learning of LPV Surrogate Models of Fuel Sloshing

This paper aims to enhance the efficiency of validation and verification campaigns involving fuel sloshing phenomena. Our first contribution is the development of an open-source, high-fidelity and computationally efficient two-dimensional smoothed-particle hydrodynamics-based fuel sloshing simulator that reproduces the dynamics of a spacecraft with a partially filled tank with liquid propellant. Implemented in Python using Jax, the simulator leverages GPU parallelization and supports automatic differentiation, enabling rapid generation of simulation data and system linearizations for general surrogate modelling purposes. Our second contribution is the demonstration of a practical methodology for constructing surrogate models of fuel sloshing from input--output data generated by the simulator, targeting rapid simulation and model-based control applications. The surrogate model employs a Linear Parameter-Varying (LPV) state-space structure with affine dependence on the scheduling variables, providing an accurate yet computationally efficient approximation of the sloshing dynamics. The capabilities of the proposed approach are demonstrated through closed-loop simulations of a rigid spacecraft with a partially filled fuel tank for two manoeuvre profiles under zero-gravity conditions. The identified surrogate enables simulations that are two orders of magnitude faster than the high-fidelity model.

eess.SY↗

arXiv2026

Learning Surrogate LPV State-Space Models with Uncertainty Quantification

The Linear Parameter-Varying (LPV) framework enables the construction of surrogate models of complex nonlinear and high-dimensional systems, facilitating efficient stability and performance analysis together with controller design. Despite significant advances in data-driven LPV modelling, existing approaches do not quantify the uncertainty of the obtained LPV models. Consequently, assessing model reliability for analysis and control or detecting operation outside the training regime requires extensive validation and user expertise. This paper proposes a Bayesian approach for the joint estimation of LPV state-space models together with their scheduling, providing a characterization of model uncertainty and confidence bounds on the predicted model response directly from input-output data. Both aleatoric uncertainty due to measurement noise and epistemic uncertainty arising from limited training data and structural bias are considered. The resulting model preserves the LPV structure required for controller synthesis while enabling computationally efficient simulation and uncertainty propagation. The approach is demonstrated on the surrogate modelling of a two-dimensional nonlinear interconnection of mass-spring-damper systems.

eess.SY↗

arXiv2026

Bayesian Optimization Based Grid Point Allocation for LPV and Robust Control

This paper investigates systematic selection of optimal grid points for grid-based Linear Parameter-Varying (LPV) and robust controller synthesis. In both settings, the objective is to identify a set of local models such that the controller synthesized for these local models will satisfy global stability and performance requirements for the entire system. Here, local models correspond to evaluations of the LPV or uncertain plant at fixed values of the scheduling signal or realizations of the uncertainty set, respectively. Then, Bayesian optimization is employed to discover the most informative points that govern the closed-loop performance of the designed LPV or robust controller for the complete system until no significant further performance increase or a user specified limit is reached. Furthermore, when local model evaluations are computationally demanding or difficult to obtain, the proposed method is capable to minimize the number of evaluations and adjust the overall computational cost to the available budget. Lastly, the capabilities of the proposed method in automatically obtaining a sufficiently informative grid set are demonstrated on three case-studies: a robust controller design for an unbalanced disk, a multi-objective robust attitude controller design for a satellite with uncertain parameters and two flexible rotating solar arrays, and an LPV controller design for a robotic arm.

eess.SY↗

arXiv2025

On-the-fly Surrogation for Complex Nonlinear Dynamics

High-fidelity models are essential for accurately capturing nonlinear system dynamics. However, simulation of these models is often computationally too expensive and, due to their complexity, they are not directly suitable for analysis, control design or real-time applications. Surrogate modelling techniques seek to construct simplified representations of these systems with minimal complexity, but adequate information on the dynamics given a simulation, analysis or synthesis objective at hand. Despite the widespread availability of system linearizations and the growing computational potential of autograd methods, there is no established approach that systematically exploits them to capture the underlying global nonlinear dynamics. This work proposes a novel surrogate modelling approach that can efficiently build a global representation of the dynamics on-the-fly from local system linearizations without ever explicitly computing a model. Using radial basis function interpolation and the second fundamental theorem of calculus, the surrogate model is only computed at its evaluation, enabling rapid computation for simulation and analysis and seamless incorporation of new linearization data. The efficiency and modelling capabilities of the method are demonstrated on simulation examples.

eess.SY↗

arXiv2025

Tensor-based reduction of linear parameter-varying state-space models

The Linear Parameter-Varying (LPV) framework is a powerful tool for controlling nonlinear and complex systems, but the conversion of nonlinear models into LPV forms often results in high-dimensional and overly conservative LPV models. To be able to apply control strategies, there is often a need for model reduction in order to reduce computational needs. This paper presents the first systematic approach for the joint reduction of state order and scheduling signal dimension of LPV state space models. The existing methods typically address these reductions separately. By formulating a tensorial form of LPV models with an affine dependency on the scheduling variables, we leverage tensor decomposition to find the dominant components of state and scheduling subspaces. We extend the common Petrov-Galerkin projection approach to LPV framework by adding a scheduling projection. This extension enables the joint reduction. To find suitable subspaces for the extended Petrov-Galerkin projection, we have developed two different methods: tensor-based LPV moment matching, and an approach through Proper Orthogonal Decomposition. Advantages of the proposed methods are demonstrated on two different series-interconnected mass-spring-damper systems with nonlinear springs: one primarily used for comparison with other methods and a more elaborate higher-order model designed to assess scalability.

eess.SY↗

arXiv2025

Automated Linear Parameter-Varying Modeling of Nonlinear Systems: A Global Embedding Approach

In this paper, an automated Linear Parameter-Varying (LPV) model conversion approach is proposed for nonlinear dynamical systems. The proposed method achieves global embedding of the original nonlinear behavior of the system by leveraging the second fundamental theorem of calculus to factorize matrix function expressions without any approximation. The implementation of the proposed method in the LPVcore toolbox for Matlab is discussed, and its performance is showcased on a comprehensive example of automated LPV model conversion of an unbalanced disk system, which is then used to design an LPV controller that is deployed on the original nonlinear system. In addition, the conversion capabilities are further demonstrated by obtaining an LPV embedding of a three-degree-of-freedom control moment gyroscope. All software implementations are available at www.lpvcore.net.

eess.SY↗

arXiv2024

On the reduction of Linear Parameter-Varying State-Space models

This paper presents an overview and comparative study of the state of the art in State-Order Reduction (SOR) and Scheduling Dimension Reduction (SDR) for Linear Parameter-Varying (LPV) State-Space (SS) models, comparing and benchmarking their capabilities, limitations and performance. The use case chosen for these studies is an interconnected network of nonlinear coupled mass spring damper systems with three different configurations, where some spring coefficients are described by arbitrary user-defined static nonlinear functions. For SOR, the following methods are compared: Linear Time-Invariant (LTI), LPV and LFR-based balanced reductions, moment matching and parameter-varying oblique projection. For SDR, the following methods are compared: Principal Component Analysis (PCA), trajectory PCA, Kernel PCA and LTI balanced truncation, autoencoders and deep neural network. The comparison reveals the most suitable reduction methods for the different benchmark configurations, from which we provide use case SOR and SDR guidelines that can be used to choose the best reduction method for a given LPV-SS model.

eess.SY↗