IEEE

Direct Bayesian Identification of Inverse Linear Systems

Abstract: The kernel-based inverse system identification framework enables accurate identification of systems with non-minimum phase dynamics, greatly expanding the potential of non-causal system ...
IEEE

State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification

Abstract: While accurate, black-box system identification models lack interpretability of the underlying system dynamics. This letter proposes State-Space Kolmogorov-Arnold Networks (SS-KAN) to ...
GitHub

Meta-Learning State Space Models

This repository includes source code for training and evaluating meta-learning for system identification and control via neural state-space models, proposed in IFAC World Congress paper: ...
GitHub

System identification, PID tuning (ZN/IMC/DS), and cascade control for a differential-drive robot in MATLAB.

Each wheel actuator is modeled as a first-order system. A PWM step input of 1 is applied and the angular velocity response is recorded. Simulated and measured wheel ...