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Found 8 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.
Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach
Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product.
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.
Intermittent Sampling in Repetitive Control: Exploiting Time-Varying Measurements
The performance increase up to the sensor resolution in repetitive control (RC) invalidates the standard assumption in RC that data is available at equidistant time instances, e. g., in systems with package loss or when exploiting timestamped data from optical encoders.
