14 papers
Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
Yu Tang, Muhammad Zakwan, Efe Balta +2
The Flexible Job Shop Scheduling Problem (FJSP) is the optimal allocation of a set of jobs to machines. Two primary challenges persist in FJSP: the unpredictable arrival of future…
Meta-Learning for Rapid Adaptation in Reference Tracking of Uncertain Nonlinear Systems
Jiaqi Yan, Ankush Chakrabarty, Niklas Schmid +2
In this paper, we address the problem of reference tracking for uncertain nonlinear systems. Since collecting data from the target system (i.e., the system of interest) is often ch…
MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models
Jiaqi Yan, Ankush Chakrabarty, Alisa Rupenyan +1
In this paper, we consider the problem of reference tracking in uncertain nonlinear systems. A neural State-Space Model (NSSM) is used to approximate the nonlinear system, where a…
Iterative Tuning of Nonlinear Model Predictive Control for Robotic Manufacturing Tasks
Deepak Ingole, Valentin Bhend, Shiva Ganesh Murali +2
Manufacturing processes are often perturbed by drifts in the environment and wear in the system, requiring control re-tuning even in the presence of repetitive operations. This pap…
Differentiable Material Point Method for the Control of Deformable Objects
Diego Bolliger, Gabriele Fadini, Markus Bambach +1
Controlling the deformation of flexible objects is challenging due to their non-linear dynamics and high-dimensional configuration space. This work presents a differentiable Materi…
Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control
Gabriele Fadini, Deepak Ingole, Tong Duy Son +1
This paper presents an auto-tuning framework for torque-based Nonlinear Model Predictive Control (nMPC), where the MPC serves as a real-time controller for optimal joint torque com…