Technology Category
Machine Learning: Calibration & Uncertainty QuantificationReasoning under Uncertainty: Uncertainty RepresentationsIntelligent Robots: State Estimation
Learning Surrogate LPV State-Space Models with Uncertainty Quantification
🤖 AI Summary
This work addresses the challenge that existing data-driven linear parameter-varying (LPV) modeling approaches struggle to quantify model uncertainty, thereby hindering reliable assessment of prediction credibility or detection of out-of-distribution operating conditions. The paper proposes a Bayesian framework that, for the first time in LPV modeling, jointly accounts for aleatoric uncertainty arising from measurement noise and epistemic uncertainty stemming from limited data and structural bias. Within this framework, both the LPV state-space model and its scheduling variables are estimated simultaneously, yielding predictive confidence intervals. The approach preserves the standard LPV structure, ensuring compatibility with subsequent controller synthesis, and demonstrates high-fidelity modeling capability and robust uncertainty quantification on a two-dimensional nonlinear mass–spring–damper interconnected system.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
LPV modeling
uncertainty quantification
surrogate models
data-driven modeling
model reliability
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian LPV modeling
uncertainty quantification
state-space models
aleatoric and epistemic uncertainty
surrogate modeling
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