53 citations · 97 across the 16 of their papers we have counts for
16 papers
Flatness-based MPC using B-splines transcription with application to a Pusher-Slider System
Thomas Neve, Tom Lefebvre, Sander De Witte +1
This work discusses the use of model predictive control (MPC) for the manipulation of a pusher-slider system. In particular we leverage the differential flatness of the pusher-slid…
A Supervisory Learning Control Framework for Autonomous & Real-time Task Planning for an Underactuated Cooperative Robotic task
Sander De Witte, Tom Lefebvre, Thijs Van Hauwermeiren +1
We introduce a framework for cooperative manipulation, applied on an underactuated manipulation problem. Two stationary robotic manipulators are required to cooperate in order to r…
Differential Flatness of Slider-Pusher Systems for Constrained Time Optimal Collision Free Path Planning
Tom Lefebvre, Sander De Witte, Thomas Neve +1
In this work we show that the differential kinematics of slider-pusher systems are differentially flat assuming quasi-static behaviour and frictionless contact. Second we demonstra…
Evolutionary sparse data-driven discovery of complex multibody system dynamics
Ehsan Askari, Guillaume Crevecoeur
The value of unknown parameters of multibody systems is crucial for prediction, monitoring, and control, sometimes estimated using a biased physics-based model leading to incorrect…
KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images
Rembert Daems, Jeroen Taets, Francis wyffels +1
We present KeyCLD, a framework to learn Lagrangian dynamics from images. Learned keypoints represent semantic landmarks in images and can directly represent state dynamics. We show…
Physics Informed LSTM Network for Flexibility Identification in Evaporative Cooling Systems
Manu Lahariya, Farzaneh Karami, Chris Develder +1
In energy intensive industrial systems, an evaporative cooling process may introduce operational flexibility. Such flexibility refers to a systems ability to deviate from its sched…
Entropy Regularised Deterministic Optimal Control: From Path Integral Solution to Sample-Based Trajectory Optimisation
Tom Lefebvre, Guillaume Crevecoeur
Sample-based trajectory optimisers are a promising tool for the control of robotics with non-differentiable dynamics and cost functions. Contemporary approaches derive from a restr…
Adaptive control of a mechatronic system using constrained residual reinforcement learning
Tom Staessens, Tom Lefebvre, Guillaume Crevecoeur
We propose a simple, practical and intuitive approach to improve the performance of a conventional controller in uncertain environments using deep reinforcement learning while main…
Risk Sensitive Path Integral Control for Infinite Horizon Problem Formulations
Tom Lefebvre, Guillaume Crevecoeur
Path Integral Control methods were developed for stochastic optimal control covering a wide class of finite horizon formulations with control affine nonlinear dynamics. Characteris…
On Entropy Regularized Path Integral Control for Trajectory Optimization
Tom Lefebvre, Guillaume Crevecoeur
In this article we present a generalised view on Path Integral Control (PIC) methods. PIC refers to a particular class of policy search methods that are closely tied to the setting…
Evolutionary-Based Sparse Regression for the Experimental Identification of Duffing Oscillator
Saeideh Khatiry Goharoodi, Kevin Dekemele, Mia Loccufier +2
In this paper, an evolutionary-based sparse regression algorithm is proposed and applied onto experimental data collected from a Duffing oscillator setup and numerical simulation d…
Neural Network Augmented Physics Models for Systems with Partially Unknown Dynamics: Application to Slider-Crank Mechanism
Wannes De Groote, Edward Kikken, Erik Hostens +2
Dynamic models of mechatronic systems are abundantly used in the context of motion control and design of complex servo applications. In practice, these systems are often plagued by…
Inverse Parametric Uncertain Identification using Polynomial Chaos and high-order Moment Matching benchmarked on a Wet Friction Clutch
Wannes De Groote, Tom Lefebvre, Georges Tod +4
A numerically efficient inverse method for parametric model uncertainty identification using maximum likelihood estimation is presented. The goal is to identify a probability model…
Polynomial Chaos reformulation in Nonlinear Stochastic Optimal Control with application on a drivetrain subject to bifurcation phenomena
Tom Lefebvre, Frederik De Belie, Guillaume Crevecoeur
This paper discusses a method enabling optimal control of nonlinear systems that are subject to parametric uncertainty. A stochastic optimal tracking problem is formulated that can…
A Radial Basis Function based Optimization Algorithm with Regular Simplex set geometry in Ellipsoidal Trust-Regions
Tom Lefebvre, Frederik De Belie, Guillaume Crevecoeur
We present a novel derivative-free interpolation based optimization algorithm. A trust-region method is used where a surrogate model is realized via an interpolation framework. The…
Sparse Identification of Nonlinear Duffing Oscillator From Measurement Data
Saeideh Khatiry Goharoodi, Kevin Dekemele, Luc Dupre +2
In this paper we aim to apply an adaptation of the recently developed technique of sparse identification of nonlinear dynamical systems on a Duffing experimental setup with cubic f…