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Found 12 papers, 0 papers with code
Grey-box Recursive Parameter Identification of a Nonlinear Dynamic Model for Mineral Flotation
This study presents a grey-box recursive identification technique to estimate key parameters in a mineral flotation process across two scenarios.
Statistical Analysis of Block Coordinate Descent Algorithms for Linear Continuous-time System Identification
Block coordinate descent is an optimization technique that is used for estimating multi-input single-output (MISO) continuous-time models, as well as single-input single output (SISO) models in additive form.
Consistency analysis of refined instrumental variable methods for continuous-time system identification in closed-loop
In this paper, we address the consistency of the simplified refined instrumental variable method for continuous-time systems (SRIVC) and its closed-loop variant CLSRIVC when they are applied on data that is generated from a feedback loop.
Identification of Additive Continuous-time Systems in Open and Closed-loop
When identifying electrical, mechanical, or biological systems, parametric continuous-time identification methods can lead to interpretable and parsimonious models when the model structure aligns with the physical properties of the system.
Nonlinear Bayesian Identification for Motor Commutation: Applied to Switched Reluctance Motors
Switched Reluctance Motors (SRMs) enable power-efficient actuation with mechanically simple designs.
On the Relation between Discrete and Continuous-time Refined Instrumental Variable Methods
The Refined Instrumental Variable method for discrete-time systems (RIV) and its variant for continuous-time systems (RIVC) are popular methods for the identification of linear systems in open-loop.
Parsimonious Identification of Continuous-Time Systems: A Block-Coordinate Descent Approach
The identification of electrical, mechanical, and biological systems using data can benefit greatly from prior knowledge extracted from physical modeling.
An EM Algorithm for Lebesgue-sampled State-space Continuous-time System Identification
This paper concerns the identification of continuous-time systems in state-space form that are subject to Lebesgue sampling.
Identifying Lebesgue-sampled Continuous-time Impulse Response Models: A Kernel-based Approach
Control applications are increasingly sampled non-equidistantly in time, including in motion control, networked control, resource-aware control, and event-triggered control.
Kernel-based identification using Lebesgue-sampled data
Sampling in control applications is increasingly done non-equidistantly in time.
Non-causal regularized least-squares for continuous-time system identification with band-limited input excitations
In continuous-time system identification, the intersample behavior of the input signal is known to play a crucial role in the performance of estimation methods.
A Finite-Sample Deviation Bound for Stable Autoregressive Processes
In this paper, we study non-asymptotic deviation bounds of the least squares estimator in Gaussian AR($n$) processes.
