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Found 6 papers, 0 papers with code
Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification
Ensuring stability of discrete-time (DT) linear parameter-varying (LPV) input-output (IO) models estimated via system identification methods is a challenging problem as known stability constraints can only be numerically verified, e. g., through solving Linear Matrix Inequalities.
Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach
The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network.
Learning for Precision Motion of an Interventional X-ray System: Add-on Physics-Guided Neural Network Feedforward Control
Tracking performance of physical-model-based feedforward control for interventional X-ray systems is limited by hard-to-model parasitic nonlinear dynamics, such as cable forces and nonlinear friction.
Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics
Unknown nonlinear dynamics often limit the tracking performance of feedforward control.
Neural Network Training Using Closed-Loop Data: Hazards and an Instrumental Variable (IVNN) Solution
An increasing trend in the use of neural networks in control systems is being observed.
Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach
The aim of this paper is to develop a feedforward control framework for systems with unknown, typically nonlinear, dynamics.
