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Found 11 papers, 4 papers with code
Model order reduction of deep structured state-space models: A system-theoretic approach
With a specific emphasis on control design objectives, achieving accurate system modeling with limited complexity is crucial in parametric system identification.
Synthetic data generation for system identification: leveraging knowledge transfer from similar systems
This paper addresses the challenge of overfitting in the learning of dynamical systems by introducing a novel approach for the generation of synthetic data, aimed at enhancing model generalization and robustness in scenarios characterized by data scarcity.
Shedding Light on the Ageing of Extra Virgin Olive Oil: Probing the Impact of Temperature with Fluorescence Spectroscopy and Machine Learning Techniques
This work systematically investigates the oxidation of extra virgin olive oil (EVOO) under accelerated storage conditions with UV absorption and total fluorescence spectroscopy.
Data-Driven Synthesis of Configuration-Constrained Robust Invariant Sets for Linear Parameter-Varying Systems
We present a data-driven method to synthesize robust control invariant (RCI) sets for linear parameter-varying (LPV) systems subject to unknown but bounded disturbances.
Parameter Dependent Robust Control Invariant Sets for LPV Systems with Bounded Parameter Variation Rate
We present a method to synthesize parameter-dependent robust control invariant (PD-RCI) sets for LPV systems with bounded parameter variation, in which invariance is induced using PD-vertex control laws.
Data-Driven Computation of Robust Invariant Sets and Gain-Scheduled Controllers for Linear Parameter-Varying Systems
We present a direct data-driven approach to synthesize robust control invariant (RCI) sets and their associated gain-scheduled feedback control laws for linear parameter-varying (LPV) systems subjected to bounded disturbances.
Direct Data-Driven Computation of Polytopic Robust Control Invariant Sets and State-Feedback Controllers
This paper presents a direct data-driven approach for computing robust control invariant (RCI) sets and their associated state-feedback control laws for linear time-invariant systems affected by bounded disturbances.
Direct identification of continuous-time linear switched state-space models
The key idea for direct CT identification is based on an integral architecture consisting of an LSS model followed by an integral block.
Learning neural state-space models: do we need a state estimator?
In recent years, several algorithms for system identification with neural state-space models have been introduced.
Deep learning with transfer functions: new applications in system identification
The dynamical operator is included as {the} last layer of a neural network in order to obtain the optimal one-step-ahead prediction error.
Computation of Parameter Dependent Robust Invariant Sets for LPV Models with Guaranteed Performance
This paper presents an iterative algorithm to compute a Robust Control Invariant (RCI) set, along with an invariance-inducing control law, for Linear Parameter-Varying (LPV) systems.
