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Found 13 papers, 12 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.
In-context learning of state estimators
State estimation has a pivotal role in several applications, including but not limited to advanced control design.
On the adaptation of in-context learners for system identification
In-context system identification aims at constructing meta-models to describe classes of systems, differently from traditional approaches that model single systems.
From system models to class models: An in-context learning paradigm
Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavior of other systems within the same class?
Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification
Effective quantification of uncertainty is an essential and still missing step towards a greater adoption of deep-learning approaches in different applications, including mission-critical ones.
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.
On the adaptation of recurrent neural networks for system identification
The system dynamics are then assumed to change, leading to an unacceptable degradation of the nominal model performance on the perturbed system.
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.
dynoNet: a neural network architecture for learning dynamical systems
This paper introduces a network architecture, called dynoNet, utilizing linear dynamical operators as elementary building blocks.
Continuous-time system identification with neural networks: Model structures and fitting criteria
This paper presents tailor-made neural model structures and two custom fitting criteria for learning dynamical systems.
Model structures and fitting criteria for system identification with neural networks
This paper focuses on the identification of dynamical systems with tailor-made model structures, where neural networks are used to approximate uncertain components and domain knowledge is retained, if available.
Efficient Calibration of Embedded MPC
Model Predictive Control (MPC) is a powerful and flexible design tool of high-performance controllers for physical systems in the presence of input and output constraints.
Systems and Control Systems and Control Optimization and Control
