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Found 32 papers, 0 papers with code
Stable Inversion of Piecewise Affine Systems with Application to Feedforward and Iterative Learning Control
To demonstrate the primary contributions' validity and utility, this article also integrates PWA stable inversion with ILC in simulations based on a physical printhead positioning system.
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.
A Frequency-Domain Approach for Enhanced Performance and Task Flexibility in Finite-Time ILC
Iterative learning control (ILC) is capable of improving the tracking performance of repetitive control systems by utilizing data from past iterations.
Closed-loop Data-Enabled Predictive Control and its equivalence with Closed-loop Subspace Predictive Control
Factors like improved data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC).
Robust Commutation Design: Applied to Switched Reluctance Motors
Switched Reluctance Motors (SRMs) are cost-effective electric actuators that utilize magnetic reluctance to generate torque, with torque ripple arising from unaccounted manufacturing defects in the rotor tooth geometry.
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.
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.
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.
Beyond Nyquist in Frequency Response Function Identification: Applied to Slow-Sampled Systems
Fast-sampled models are essential for control design, e. g., to address intersample behavior.
Direct Shaping of Minimum and Maximum Singular Values: An $\mathcal{H}_{-}/\mathcal{H}_{\infty}$ Synthesis Approach for Fault Detection Filters
The performance of fault detection filters relies on a high sensitivity to faults and a low sensitivity to disturbances.
Cascaded Calibration of Mechatronic Systems via Bayesian Inference
Sensors in high-precision mechatronic systems require accurate calibration, which is achieved using test beds that, in turn, require even more accurate calibration.
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.
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.
A Kernel-Based Identification Approach to LPV Feedforward: With Application to Motion Systems
The aim of this paper is to develop an identification approach that directly identifies dynamically scheduled feedforward controllers for LPV motion systems from data.
Kernel-based identification using Lebesgue-sampled data
Sampling in control applications is increasingly done non-equidistantly in time.
Basis Function feedforward for Position-Dependent Systems
Feedforward for motion systems is getting increasingly more important to achieve performance requirements.
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.
Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach
Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput.
Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression
Switched reluctance motors are appealing because they are inexpensive in both construction and maintenance.
Automated MIMO Motion Feedforward Control: Efficient Learning through Data-Driven Gradients via Adjoint Experiments and Stochastic Approximation
To this end, a cost criterion is minimized using a stochastic gradient descent algorithm, in which both the search direction and step size are determined through system experiments.
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.
Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control
Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model.
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.
Position-Dependent Snap Feedforward: A Gaussian Process Framework
Position-dependent compliance is compensated for by using a Gaussian process to model the snap feedforward parameter as a continuous function of position.
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder
Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control.
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.
Learning nonlinear feedforward: a Gaussian Process Approach Applied to a Printer with Friction
Feedforward control is essential to achieving good tracking performance in positioning systems.
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.
Conjugate gradient MIMO iterative learning control using data-driven stochastic gradients
Data-driven iterative learning control can achieve high performance for systems performing repeating tasks without the need for modeling.
Iterative learning control with discrete-time nonlinear nonminimum phase models via stable inversion
Specifically, this article facilitates ILC of such systems by presenting a new ILC synthesis framework that allows combination of the principles of Newton's root finding algorithm with stable inversion, a technique for generating stable trajectories from unstable models.
Frequency-Domain Data-Driven Controller Synthesis for Unstable LPV Systems
Synthesizing controllers directly from frequency-domain measurement data is a powerful tool in the linear time-invariant framework.
