6 papers
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…
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…