Controlling Chaos Using Edge Computing Hardware
- URL: http://arxiv.org/abs/2406.12876v1
- Date: Wed, 8 May 2024 21:11:00 GMT
- Title: Controlling Chaos Using Edge Computing Hardware
- Authors: Robert M. Kent, Wendson A. S. Barbosa, Daniel J. Gauthier,
- Abstract summary: We show that a nonlinear controller can tackle a difficult control problem.
The model is accurate, yet it is small enough to be evaluated on a field-programmable gate array.
Our work represents the first step in deploying efficient machine learning algorithms to the computing "edge"
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Machine learning provides a data-driven approach for creating a digital twin of a system - a digital model used to predict the system behavior. Having an accurate digital twin can drive many applications, such as controlling autonomous systems. Often the size, weight, and power consumption of the digital twin or related controller must be minimized, ideally realized on embedded computing hardware that can operate without a cloud-computing connection. Here, we show that a nonlinear controller based on next-generation reservoir computing can tackle a difficult control problem: controlling a chaotic system to an arbitrary time-dependent state. The model is accurate, yet it is small enough to be evaluated on a field-programmable gate array typically found in embedded devices. Furthermore, the model only requires 25.0 $\pm$ 7.0 nJ per evaluation, well below other algorithms, even without systematic power optimization. Our work represents the first step in deploying efficient machine learning algorithms to the computing "edge."
Related papers
- Unraveling the Control Engineer's Craft with Neural Networks [4.5088302622486935]
We present a data-driven controller tuning approach, where the digital twin is used to generate input-output data and suitable controllers for several perturbations in its parameters.
We learn the controller tuning rule that maps input-output data onto the controller parameters, based on artificially generated data from perturbed versions of the digital twin.
arXiv Detail & Related papers (2023-11-20T10:22:38Z) - Data-Driven H-infinity Control with a Real-Time and Efficient
Reinforcement Learning Algorithm: An Application to Autonomous
Mobility-on-Demand Systems [3.5897534810405403]
This paper presents a model-free, real-time, data-efficient Q-learning-based algorithm to solve the H$_infty$ control of linear discrete-time systems.
An adaptive optimal controller is designed and the parameters of the action and critic networks are learned online without the knowledge of the system dynamics.
arXiv Detail & Related papers (2023-09-16T05:02:41Z) - Controlling dynamical systems to complex target states using machine
learning: next-generation vs. classical reservoir computing [68.8204255655161]
Controlling nonlinear dynamical systems using machine learning allows to drive systems into simple behavior like periodicity but also to more complex arbitrary dynamics.
We show first that classical reservoir computing excels at this task.
In a next step, we compare those results based on different amounts of training data to an alternative setup, where next-generation reservoir computing is used instead.
It turns out that while delivering comparable performance for usual amounts of training data, next-generation RC significantly outperforms in situations where only very limited data is available.
arXiv Detail & Related papers (2023-07-14T07:05:17Z) - A Robust and Explainable Data-Driven Anomaly Detection Approach For
Power Electronics [56.86150790999639]
We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer.
The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data.
A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
arXiv Detail & Related papers (2022-09-23T06:09:35Z) - Digital Twin-based Intrusion Detection for Industrial Control Systems [0.5459797813771499]
This study contributes to a digital twin-based security framework for industrial control systems.
Four types of process-aware attack scenarios are implemented on a standalone open-source digital twin of an industrial filling plant.
A stacked ensemble is proposed as the real-time intrusion detection, based on the offline evaluation of eight supervised machine learning algorithms.
arXiv Detail & Related papers (2022-07-20T16:03:10Z) - A neural network based heading and position control system of a ship [0.0]
Heading and position control system of ships has remained a challenging control problem.
An artificial neural network controller is proposed for heading and position control system.
arXiv Detail & Related papers (2022-04-02T04:21:31Z) - Automatic digital twin data model generation of building energy systems
from piping and instrumentation diagrams [58.720142291102135]
We present an approach to recognize symbols and connections of P&ID from buildings in a completely automated way.
We apply algorithms for symbol recognition, line recognition and derivation of connections to the data sets.
The approach can be used in further processes like control generation, (distributed) model predictive control or fault detection.
arXiv Detail & Related papers (2021-08-31T15:09:39Z) - Machine learning based digital twin for stochastic nonlinear
multi-degree of freedom dynamical system [0.0]
We propose a novel digital twin framework for nonlinear multi-degree of freedom (DOFM) dynamical systems.
The proposed framework can be used with any choice of Bayesian filtering and machine learning algorithm.
Results obtained indicate the applicability and excellent performance of the proposed digital twin framework.
arXiv Detail & Related papers (2021-03-29T14:14:06Z) - A Novel Anomaly Detection Algorithm for Hybrid Production Systems based
on Deep Learning and Timed Automata [73.38551379469533]
DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems.
It combines deep learning and timed automata for creating behavioral model from observations.
The algorithm has been applied to few data sets including two from real systems and has shown promising results.
arXiv Detail & Related papers (2020-10-29T08:27:43Z) - Binary DAD-Net: Binarized Driveable Area Detection Network for
Autonomous Driving [94.40107679615618]
This paper proposes a novel binarized driveable area detection network (binary DAD-Net)
It uses only binary weights and activations in the encoder, the bottleneck, and the decoder part.
It outperforms state-of-the-art semantic segmentation networks on public datasets.
arXiv Detail & Related papers (2020-06-15T07:09:01Z) - One-step regression and classification with crosspoint resistive memory
arrays [62.997667081978825]
High speed, low energy computing machines are in demand to enable real-time artificial intelligence at the edge.
One-step learning is supported by simulations of the prediction of the cost of a house in Boston and the training of a 2-layer neural network for MNIST digit recognition.
Results are all obtained in one computational step, thanks to the physical, parallel, and analog computing within the crosspoint array.
arXiv Detail & Related papers (2020-05-05T08:00:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.