On the Universal Transformation of Data-Driven Models to Control Systems

02/09/2021
by   Sebastian Peitz, et al.
0

As in almost every other branch of science, the major advances in data science and machine learning have also resulted in significant improvements regarding the modeling and simulation of nonlinear dynamical systems. It is nowadays possible to make accurate medium to long-term predictions of highly complex systems such as the weather, the dynamics within a nuclear fusion reactor, of disease models or the stock market in a very efficient manner. In many cases, predictive methods are advertised to ultimately be useful for control, as the control of high-dimensional nonlinear systems is an engineering grand challenge with huge potential in areas such as clean and efficient energy production, or the development of advanced medical devices. However, the question of how to use a predictive model for control is often left unanswered due to the associated challenges, namely a significantly higher system complexity, the requirement of much larger data sets and an increased and often problem-specific modeling effort. To solve these issues, we present a universal framework (which we call QuaSiModO: Quantization-Simulation-Modeling-Optimization) to transform arbitrary predictive models into control systems and use them for feedback control. The advantages of our approach are a linear increase in data requirements with respect to the control dimension, performance guarantees that rely exclusively on the accuracy of the predictive model, and only little prior knowledge requirements in control theory to solve complex control problems. In particular the latter point is of key importance to enable a large number of researchers and practitioners to exploit the ever increasing capabilities of predictive models for control in a straight-forward and systematic fashion.

READ FULL TEXT
08/26/2021

Data-driven modeling and control of large-scale dynamical systems in the Loewner framework

In this contribution, we discuss the modeling and model reduction framew...
01/04/2018

Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems

The process of transforming observed data into predictive mathematical m...
05/24/2019

Deep Model Predictive Control with Online Learning for Complex Physical Systems

The control of complex systems is of critical importance in many branche...
02/23/2021

Controlling nonlinear dynamical systems into arbitrary states using machine learning

We propose a novel and fully data driven control scheme which relies on ...
05/22/2023

Multirotor Ensemble Model Predictive Control I: Simulation Experiments

Nonlinear receding horizon model predictive control is a powerful approa...
05/19/2015

Towards Data-Driven Autonomics in Data Centers

Continued reliance on human operators for managing data centers is a maj...

Code Repositories

QuaSiModO

Construction of control systems from predictive models via Quantization, Simulation, Modeling, Optimization


view repo

Please sign up or login with your details

Forgot password? Click here to reset