Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection

2025-03-24 · Joshua Näf, Keith Moffat, Jaap Eising, Florian Dörfler

This paper proposes Select-Data-driven Predictive Control (Select-DPC), a new method for controlling nonlinear systems using output-feedback for which data are available but an explicit model is not. At each timestep, Select-DPC employs only the most relevant data to implicitly linearize the dynamics in "trajectory space". Then, taking user-defined output constraints into account, it makes control decisions using a convex optimization. This optimal control is applied in a receding-horizon manner. As the online data-selection is the core of Select-DPC, we propose and verify both norm-based and manifold-embedding-based selection methods. We evaluate Select-DPC on three benchmark nonlinear system simulators -- rocket-landing, a robotic arm and cart-pole inverted pendulum swing-up -- comparing them with standard Data-enabled Predictive Control (DeePC) and Time-Windowed DeePC methods, and find that Select-DPC outperforms both methods.

📄 PDF Abstract BibTeX arXiv:2503.18845

Code (1)

naefjo/choose-wisely-paper 공식 구현

Receding Horizon Optimization for Disturbance-aware Predictive Control of Power Electronic Inverters

2020-12-22 · Zhengxi Chen, Xun Shen

A disturbance-aware predictive control policy is proposed for DC-AC power inverters with the receding horizon optimization approach. First, a discrete event-driven hybrid automaton model has been constructed for the nonl…

Spending Privacy Budget Fairly and Wisely

2022-04-27 · Lucas Rosenblatt, Joshua Allen, Julia Stoyanovich

Differentially private (DP) synthetic data generation is a practical method for improving access to data as a means to encourage productive partnerships. One issue inherent to DP is that the "privacy budget" is generally…

Fairness Feature Importance Synthetic Data Generation

Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration

2015-09-23 · Christopher Xie, Sachin Patil, Teodor Moldovan, Sergey Levine 외

In this paper, we present a robotic model-based reinforcement learning method that combines ideas from model identification and model predictive control. We use a feature-based representation of the dynamics that allows …

Model-based Reinforcement Learning Model Predictive Control reinforcement-learning Reinforcement Learning+1

Un jeu a debattre pour sensibiliser a l'Intelligence Artificielle dans le contexte de la pandemie de COVID-19

2023-04-19 · Carole Adam, Cédric Lauradoux

Artificial Intelligence is more and more pervasive in our lives. Many important decisions are delegated to AI algorithms: accessing higher education, determining prison sentences, autonomously driving vehicles... Enginee…

Data-driven predictive control with estimated prediction matrices and integral action

2021-04-11 · P. C. N. Verheijen, G. R. Gonçalves da Silva, M. Lazar

This paper presents a data-driven approach to the design of predictive controllers. The prediction matrices utilized in standard model predictive control (MPC) algorithms are typically constructed using knowledge of a sy…

Model Predictive Control Position Prediction