Search Results for author:
Found 27 papers, 0 papers with code
Online Residual Learning from Offline Experts for Pedestrian Tracking
In this paper, we consider the problem of predicting unknown targets from data.
In-situ Controller Autotuning by Bayesian Optimization for Closed-loop Feedback Control of Laser Powder Bed Fusion Process
In summary, BO presents a promising method for automatic in-layer controller tuning in LPBF, enhancing control precision and mitigating overheating in production parts.
Adaptive Bayesian Optimization for High-Precision Motion Systems
We further evaluate the algorithm's performance on a real precision-motion system utilized in semiconductor industry applications by modifying the payload and reference stepsize and comparing it to an interpolated constrained optimization-based baseline approach.
On the Regret of Recursive Methods for Discrete-Time Adaptive Control with Matched Uncertainty
Continuous-time adaptive controllers for systems with a matched uncertainty often comprise an online parameter estimator and a corresponding parameterized controller to cancel the uncertainty.
Force Controlled Printing for Material Extrusion Additive Manufacturing
In material extrusion additive manufacturing, the extrusion process is commonly controlled in a feed-forward fashion.
Data-Enabled Predictive Iterative Control
This work introduces the Data-Enabled Predictive iteRative Control (DeePRC) algorithm, a direct data-driven approach for iterative LTI systems.
Online Identification of Stochastic Continuous-Time Wiener Models Using Sampled Data
It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators.
Closed-loop Performance Optimization of Model Predictive Control with Robustness Guarantees
Model mismatch and process noise are two frequently occurring phenomena that can drastically affect the performance of model predictive control (MPC) in practical applications.
Predictive Linear Online Tracking for Unknown Targets
The learned model is used in the optimal policy under the framework of receding horizon control.
Closed-Loop Finite-Time Analysis of Suboptimal Online Control
Finite-time guarantees allow the control design to distribute a limited computational budget over a time horizon and estimate the on-the-go loss in performance due to sub-optimality.
Layer-to-Layer Melt Pool Control in Laser Powder Bed Fusion
Additive manufacturing processes are flexible and efficient technologies for producing complex geometries.
Efficient safe learning for controller tuning with experimental validation
The performance of the new method is first validated in a simulated precision motion system, demonstrating improved computational efficiency, and illustrating the role of exploiting numerical solvers to reach the desired precision.
Sequential Quadratic Programming-based Iterative Learning Control for Nonlinear Systems
We present simulations to illustrate the performance of the proposed method for linear and nonlinear dynamics models.
Safe Risk-averse Bayesian Optimization for Controller Tuning
Controller tuning and parameter optimization are crucial in system design to improve both the controller and underlying system performance.
On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control
Inexact methods for model predictive control (MPC), such as real-time iterative schemes or time-distributed optimization, alleviate the computational burden of exact MPC by providing suboptimal solutions.
Online Linear Quadratic Tracking with Regret Guarantees
Online learning algorithms for dynamical systems provide finite time guarantees for control in the presence of sequentially revealed cost functions.
A Model Predictive Control Framework for Improving Risk-Tolerance of Manufacturing Systems
The need for control strategies that can address dynamic system uncertainty is becoming increasingly important.
Stochastic Wasserstein Gradient Flows using Streaming Data with an Application in Predictive Maintenance
Since estimation problems can be posed as optimization problems in the probability space, we devise a stochastic projected Wasserstein gradient flow that keeps track of the belief of the estimated quantity and can consume samples from online data.
Implications of Regret on Stability of Linear Dynamical Systems
The setting of an agent making decisions under uncertainty and under dynamic constraints is common for the fields of optimal control, reinforcement learning, and recently also for online learning.
Data-Driven Process Optimization of Fused Filament Fabrication based on In Situ Measurements
The tuning of fused filament fabrication parameters is notoriously challenging.
Risk-Averse Model Predictive Control for Priced Timed Automata
In this paper, we propose a Risk-Averse Priced Timed Automata (PTA) Model Predictive Control (MPC) framework to increase flexibility of cyber-physical systems.
Controller-Aware Dynamic Network Management for Industry 4.0
In this paper, we consider a cyber-physical manufacturing system (CPMS) scenario containing physical components (robots, sensors, and actuators), operating in a digitally connected, constrained environment to perform industrial tasks.
Regret Analysis of Online Gradient Descent-based Iterative Learning Control with Model Mismatch
In Iterative Learning Control (ILC), a sequence of feedforward control actions is generated at each iteration on the basis of partial model knowledge and past measurements with the goal of steering the system toward a desired reference trajectory.
On Robustness in Optimization-Based Constrained Iterative Learning Control
Iterative learning control (ILC) is a control strategy for repetitive tasks wherein information from previous runs is leveraged to improve future performance.
Learning-Based Repetitive Precision Motion Control with Mismatch Compensation
In this work, we develop an iterative approach for repetitive precision motion control problems where the objective is to follow a reference geometry with minimal tracking error.
Batch Model Predictive Control for Selective Laser Melting
Selective laser melting is a promising additive manufacturing technology enabling the fabrication of highly customizable products.
In-layer Thermal Control of a Multi-layer Selective Laser Melting Process
Selective Laser Melting (SLM) is an additive manufacturing technology that builds three dimensional parts by melting layers of metal powder together with a laser that traces out a desired geometry.
