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Found 28 papers, 0 papers with code
Singularity-Avoidance Control of Robotic Systems with Model Mismatch and Actuator Constraints
Singularities, manifesting as special configuration states, deteriorate robot performance and may even lead to a loss of control over the system.
Iterative Learning Control with Mismatch Compensation for Residual Vibration Suppression in Delta Robots
We proposed an iterative learning controller for the delta robot to improve tracking accuracy.
Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
Evaluation on a realistic case study with gas compressors confirms that TVSafeOpt ensures safety when solving time-varying optimization problems with unknown reward and safety functions.
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.
MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models
The framework consists of two phases: the (offine) meta-training phase learns a aggregated NSSM using data from source systems, and the (online) meta-inference phase quickly adapts this aggregated model to the target system using only a few data points and few online training iterations, based on local loss function gradients.
Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin
This article presents the guided Bayesian optimization algorithm as an efficient data-driven method for iteratively tuning closed-loop controller parameters using an event-triggered digital twin of the system based on available closed-loop data.
Force Controlled Printing for Material Extrusion Additive Manufacturing
In material extrusion additive manufacturing, the extrusion process is commonly controlled in a feed-forward fashion.
Tuning of Online Feedback Optimization for setpoint tracking in centrifugal compressors
Online Feedback Optimization (OFO) controllers steer a system to its optimal operating point by treating optimization algorithms as auxiliary dynamic systems.
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.
Drone-based Volume Estimation in Indoor Environments
Volume estimation in large indoor spaces is an important challenge in robotic inspection of industrial warehouses.
Data-Driven Process Optimization of Fused Filament Fabrication based on In Situ Measurements
The tuning of fused filament fabrication parameters is notoriously challenging.
Meta-Learning Priors for Safe Bayesian Optimization
In the presence of unknown safety constraints, it is crucial to choose reliable model hyper-parameters to avoid safety violations.
Data-driven Reference Trajectory Optimization for Precision Motion Systems
We propose a data-driven optimization-based pre-compensation method to improve the contour tracking performance of precision motion stages by modifying the reference trajectory and without modifying any built-in low-level controllers.
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.
Advanced Manufacturing Configuration by Sample-efficient Batch Bayesian Optimization
We propose a framework for the configuration and operation of expensive-to-evaluate advanced manufacturing methods, based on Bayesian optimization.
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.
Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization
Accurate positioning and fast traversal times determine the productivity in machining applications.
Plasma Spray Process Parameters Configuration using Sample-efficient Batch Bayesian Optimization
Recent work has shown constrained Bayesian optimization to be a powerful technique for the optimization of industrial processes.
Safe and Efficient Model-free Adaptive Control via Bayesian Optimization
Thus, our approach builds on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization.
Learning from Simulation, Racing in Reality
We present a reinforcement learning-based solution to autonomously race on a miniature race car platform.
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization
This paper presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization.
