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Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling [article]

Nishant Yadav, Sai Ravela, Auroop R. Ganguly
2020 arXiv   pre-print
Systems exhibiting nonlinear dynamics, including but not limited to chaos, are ubiquitous across Earth Sciences such as Meteorology, Hydrology, Climate and Ecology, as well as Biology such as neural and  ...  Climate scientists have pointed to Machine Learning enhanced parameter estimation as a possible solution, with proof-of-concept methodological adaptations being examined on idealized systems.  ...  A schematic of how Machine Learning can inform ESMs -along with the connection to an idealized Nonlinear Dynamical System (specifically, the L96-2L) often used as a proxy model -is shown schematically  ... 
arXiv:2008.05590v1 fatcat:l2gnge47hbgf3fi2i65xxapm5y

Trends in Theory of Control System Design Status report prepared by the IFAC Coordinating Committee on Design Methods

Ruth Bars, Patrizio Colaneri, Luc Dugard, Frank Allgöwer, Anatolii Kleimenov, Carsten Scherer
2008 IFAC Proceedings Volumes  
Control theory deals with disciplines and methods leading to an automatic decision process in order to improve the performance of a control system.  ...  The evolution of control engineering is closely related to the evolution of the technology of sensors and actuators, and to the theoretical controller design methods and numerical techniques to be applied  ...  Several techniques from machine learning proved successful in identification of linear and nonlinear systems.  ... 
doi:10.3182/20080706-5-kr-1001.00363 fatcat:hlkx54q5uvha3a6ajbebjbwjrq

Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review

Mohammad Fahmi Pairan, Department of Aeronautical Engineering, Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, 86400 Batu Pahat, Johor, MALAYSIA, Syariful Syafiq Shamsudin, Mohd Fadhli Zulkafli, Department of Aeronautical Engineering, Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, 86400 Batu Pahat, Johor, MALAYSIA, Department of Aeronautical Engineering, Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, 86400 Batu Pahat, Johor, MALAYSIA
2020 Journal of Advanced Mechanical Engineering Applications  
In order to design a robust control algorithm, it is crucial to obtain a precise quadrotor flight dynamics through system identification approach.  ...  System identification is a method of finding the mathematical model of the dynamics system using the input-output data measurement.  ...  The work presented in this paper attempts to review the development of a system identification method based on the NN model for quadcopter dynamic modeling application.  ... 
doi:10.30880/ijie.2020.02.01.003 fatcat:gzabba3wl5cafh45qc7fvfc2re

2019 Index IEEE Systems Journal Vol. 13

2019 IEEE Systems Journal  
., +, JSYST March 2019 760-770 Hybrid Machine Learning System to Forecast Electricity Consumption of Smart Grid-Based Air Conditioners.  ...  ., +, JSYST Sept. 2019 3347-3357 Load forecasting Hybrid Machine Learning System to Forecast Electricity Consumption of Smart Grid-Based Air Conditioners.  ... 
doi:10.1109/jsyst.2020.2965224 fatcat:x7hx4luelzforfuzigmwmfyhgm

Power system stability agents using robust wide area control

Hui Ni, G.T. Heydt, L. Mili
2002 IEEE Transactions on Power Systems  
The robustness of the proposed controller is capable of compensating for the nonlinear dynamic operation of power systems and uncertain disturbances.  ...  The performance of the robust controller as a power system stability agent is studied using a 29-machine 179-bus power system example.  ...  Farmer for his helpful suggestions.  ... 
doi:10.1109/tpwrs.2002.805016 fatcat:yagfmrfs4nfz5pyzclslgmzfla

Table of Contents

2020 IEEE Transactions on Aerospace and Electronic Systems  
Khoshsima 2212 Robust Distributed Parameter Estimation of Nonlinear Systems With Missing Data Over Networks . . . . . . . . . . . . . . . . . . .  ...  Johansen 2157 Improved Model for Micro-UAV Propulsion Systems: Characterization and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/taes.2020.2992377 fatcat:wq2hwjpva5blvlo2fryvg5twre

Power System Stability Agents Using Robust Wide-Area Control

Hui Ni, Gerald T. Heydt, Lamine Mili
2002 IEEE Power Engineering Review  
The robustness of the proposed controller is capable of compensating for the nonlinear dynamic operation of power systems and uncertain disturbances.  ...  The performance of the robust controller as a power system stability agent is studied using a 29-machine 179-bus power system example.  ...  Farmer for his helpful suggestions.  ... 
doi:10.1109/mper.2002.4312586 fatcat:xa3r4emfprei5lvuaulvavcsxy

Managing Complex and Dynamic Systems for the Future [chapter]

E.D. Jones
2005 Systems and Human Science  
The challenges of modern complicated systems regarding their design, analysis, and management are put in a historical context to better propose a framework for the future involving complementary uses of  ...  testing, modeling, and performance functions.  ...  This, of course, requires another type of abstraction, and partial view, of the complex system involving a choice of mechanisms to model and preferences for the generation of certain types of system data  ... 
doi:10.1016/b978-044451813-2/50005-1 fatcat:lb6wn42r6zfcbbu6x7tq6qulg4

Driven by Data or Derived through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques with Cyber-Physical System (CPS) Focus

Rahul Rai, Chandan K. Sahu
2020 IEEE Access  
There are mainly two approaches to modeling: Physics/Equation based modeling (Model-Based, MB) and Machine Learning (ML).  ...  INDEX TERMS Cyber-physical systems, deep learning, deep neural networks, hybrid models, model-based, machine learning, physics guided, physics informed, physics prior, theory guided.  ...  Sparse Identification of Nonlinear Dynamics (SINDy) [235] used a thresholded least square-based approach to establish a relationship between the target function and a library of candidate functions consisting  ... 
doi:10.1109/access.2020.2987324 fatcat:xaltpychlfcz7cec4jrdadhxem

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

Rini Akmeliawati, Safanah M. Raafat
2013 2013 9th Asian Control Conference (ASCC)  
Qiugang Lu; Witold Pawlus; Hamid Reza Karimi*; Kjell Gunnar Robbersmyr 321 Wavelet Network based Online Sequential Extreme Learning Machine for Dynamic System Modeling Samsul Noor*; Dhiadeen  ...  zineb abazi* Numerical Solution for a Class of pursuit-evasion Problem in Low Earth Orbit Songtao Sun*; qiuhua zhang 111 A Measurement Based Approach to Mechanical Systems Navid Mohsenizadeh; Hazem  ... 
doi:10.1109/ascc.2013.6606363 dblp:conf/ascc/AkmeliawatiR13 fatcat:l7enyvdgurhpdl6yetwsst3u7y

2021 Index IEEE Transactions on Power Systems Vol. 36

2021 IEEE Transactions on Power Systems  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  Moradi-Sepahvand, A Confidence-Aware Machine Learning Framework for Dynamic Security Nonlinear equations COVID-19 Lockdown in France.  ... 
doi:10.1109/tpwrs.2021.3125235 fatcat:n3ecyy2flnapzjz7clyrp7sx4a

Principles and applications of chaotic systems

William Ditto, Toshinori Munakata
1995 Communications of the ACM  
Our notions of physical motion or dynamic systems have encompassed the precise clock-like ticking of periodic systems and the vagaries of dice-throwing chance, but have often been overlooked as a way to  ...  It's called a chaotic system, or chaos for short [5] . Chaos is all around us.  ...  Acknowledgments The authors are indebted to Kazuyuki Aihara and Ryu Katayama for their valuable information.  ... 
doi:10.1145/219717.219797 fatcat:qvnqtdfqubcphbi4vesctke2we

Towards Fault-Tolerant Strategy in Satellite Attitude Control Systems: A Review

Hicham HENNA, Houari TOUBAKH, Mohamed Redouane Kafi, Moamar SAYED-MOUCHAWEH
2020 Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM  
Spacecrafts are known to be very complex engineering systems where many technological devices enter in interaction to guarantee the overall mission objectives.  ...  This paper aims at discussing the state-of-the-art approaches proposed to guarantee the satellites' attitude control system (ACS) performances when its components suffer from faults.  ...  Another key feature resides in artificial intelligence capability to deal with system dynamics nonlinearities.  ... 
doi:10.36001/phmconf.2020.v12i1.1272 fatcat:nveqdgozqbe4fnj3gzo2j5atu4

Deep reinforcement learning in World-Earth system models to discover sustainable management strategies [article]

Felix M. Strnad, Wolfram Barfuss, Jonathan F. Donges, Jobst Heitzig
2019 arXiv   pre-print
Increasingly complex, non-linear World-Earth system models are used for describing the dynamics of the biophysical Earth system and the socio-economic and socio-cultural World of human societies and their  ...  In this work, we propose to combine recently developed machine learning techniques, namely deep reinforcement learning (DRL), with classical analysis of trajectories in the World-Earth system.  ...  ACKNOWLEDGEMENTS This work was developed in the context of the COPAN collaboration at the Potsdam Institute for Climate Impact  ... 
arXiv:1908.05567v1 fatcat:rfwgh26ftbbcvnb4y6zej3nc3e

Theory, algorithms and technology in the design of control systems

Ruth Bars, Patrizio Colaneri, Carlos E. de Souza, Luc Dugard, Frank Allgöwer, Anatolii Kleimenov, Carsten Scherer
2006 Annual Reviews in Control  
Challenges for future theoretical work are modelling, analysis and design of systems in quite new applications fields.  ...  Design of very large distributed systems has presented a new challenge to control theory including robust control. Control over the networks becomes an important application area.  ...  A renewed interest is expected in areas as machine learning, statistical estimation and system identification. Relations of data with dynamics and feedback have to be analyzed.  ... 
doi:10.1016/j.arcontrol.2006.01.006 fatcat:c6jcpbixwvc63hch4hi5pnrxx4
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