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Neural networks for modeling and control of dynamic systems: a practitioner's handbook

Derong Liu
2002 Automatica  
Neural Networks for Modeling and Control of Dynamic Systems deals with control problems of unknown nonlinear dynamical systems using neural networks.  ...  On the other hand, to control engineers, neurocontrol, or neural networks for controls, brings about some new and fresh ideas to the modeling and control design for nonlinear dynamical systems.  ...  Neural Networks for Modeling and Control of Dynamic Systems deals with control problems of unknown nonlinear dynamical systems using neural networks.  ... 
doi:10.1016/s0005-1098(02)00050-x fatcat:6vimzvpok5cobldqhacvew7soa

A Survey on Machine Learning Applied to Dynamic Physical Systems [article]

Sagar Verma
2020 arXiv   pre-print
This survey is on recent advancements in the intersection of physical modeling and machine learning. We focus on the modeling of nonlinear systems which are closer to electric motors.  ...  Survey on motor control and fault detection in operation of electric motors has been done.  ...  Recurrent Neural Network controller In [33] a model-following adaptive control structure is proposed for the speed control of a nonlinear motor drive system and the compensation of the nonlinearities  ... 
arXiv:2009.09719v2 fatcat:7vcu6wzzg5ehhb2un5cb2gafku

Identification and Adaptive Control of Dynamic Nonlinear Systems Using Sigmoid Diagonal Recurrent Neural Network

Tarek Aboueldahab, Mahumod Fakhreldin
2011 Intelligent Control and Automation  
The goal of this paper is to introduce a new neural network architecture called Sigmoid Diagonal Recurrent Neural Network (SDRNN) to be used in the adaptive control of nonlinear dynamical systems.  ...  The simulation results showed that the (SDRNN) is more efficient and accurate than the DRNN in both the identification and adaptive control of nonlinear dynamical systems.  ...  Neural Network in Nonlinear System Identification and Control In the identification stage of the adaptive control of nonlinear dynamical system, a neural network identifier model for the system to be controlled  ... 
doi:10.4236/ica.2011.23021 fatcat:odixjpmdangjtjjpnoqt7jwadm

Page 1381 of American Society of Civil Engineers. Collected Journals Vol. 121, Issue 12 [page]

1995 American Society of Civil Engineers. Collected Journals  
CONCLUSIONS In this paper, a neural network technique for structural dynamic model identification is presented.  ...  F., Chassiakos, A. G., and Caughey, T. K. (1992). “‘Structure- unknown nonlinear dynamic systems: Identification through neural networks.” Smart Struct., 1, 45-56. Masri, S. F., Chassiakos, A.  ... 

A generalized procedure in designing recurrent neural network identification and control of time-varying-delayed nonlinear dynamic systems

Xueli Wu, Jianhua Zhang, Quanmin Zhu
2010 Neurocomputing  
A generalized procedure in designing recurrent neural network identification and control of time-varying-delayed nonlinear dynamic systems. Neurocomputing, 73 (7-9).  ...  Simply the new system structure includes the previous one in one of its subsets. 2) With regarding to neural network enhanced adaptive control, a lot of papers have proposed the learning law for neural  ...  In regarding of neural network enhancement to approximate complex systems, the potential studies will cover to investigate new structure of NNs and new adaptive laws to adjust the weights of the NNs in  ... 
doi:10.1016/j.neucom.2009.12.002 fatcat:n4nbn6476bhjddr23nab55peu4

Adjustment Strategy for Dynamic Tracking Neuro-Fuzzy Controller

Kaijun Xu, Guangming Zhang, Yang Xu
2011 Procedia Engineering  
In this method, DTNC consists of two neural network composed of the same structure, one for control, one for learning.  ...  This paper presents an adjustment strategy for a dynamic tracking neuro-fuzzy controller (DTNC) for steady-state control in complex system.  ...  − Λ � � (10) System Adaptation with Dynamic Neuro-fuzzy Control System From (4) we know a neural network cannot match a nonlinear system exactly, the modeling error t f depends on the structure of the  ... 
doi:10.1016/j.proeng.2011.11.2460 fatcat:orhhyggiczd6zlfjm2xvo73qdu

Dynamic Neural Networks for Model-Free Control and Identification

Alex Poznyak, Isaac Chairez, Haibo He, Wen Yu
2012 Journal of Control Science and Engineering  
For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used.  ...  Yu "Robust adaptive control via neural linearization and compensation" proposes a new type of neural adaptive control via dynamic neural networks.  ...  Acknowledgments The editors wish to thank the editorial board for providing the opportunity to edit this special issue on modeling and adaptive control with dynamic neural networks.  ... 
doi:10.1155/2012/916340 fatcat:jslq5tod7jgepi2ylkkkgcuyxq

Adaptive control of discrete-time nonlinear systems using recurrent neural networks

L. Jin, M.M. Gupta, P.N. Nikiforuk
1994 IEE Proceedings - Control Theory and Applications  
A learning and adaptive control scheme for a general class of unknown MIMO discretetime nonlinear systems using multilayered recurrent neural networks (MRNNs) is presented.  ...  Based on the dynamic neural model, an extension of the concept of the input-output linearisation of discrete-time nonlinear systems is used to synthesise a control technique for model reference control  ...  Introduction The objective of neural networks-based adaptive control systems for unknown nonlinear plants is to develop algorithms for identification and control using neural networks through a learning  ... 
doi:10.1049/ip-cta:19949976 fatcat:nxismquxvjae3klqjgi7uogeti

Mean Derivatives Based Neural Euler Integrator For Nonlinear Dynamic Systems Modeling

Paulo M. Tasinaffo, Atair Rios Neto
2005 Learning and Nonlinear Models  
The usual approach to nonlinear dynamic systems neural modeling has been that of training a feed forward neural network to represent a discrete nonlinear input-output NARMA (Nonlinear Auto Regressive Moving  ...  In this new approach, instead of using the neural network to learn the instantaneous derivative function of the ordinary differential equation (ODE) that describes the dynamic system, it is used to learn  ...  , one concludes that it is possible to use a neural network to represent a dynamic system with the Euler integration structure for a given step size t Δ .  ... 
doi:10.21528/lnlm-vol3-no2-art5 fatcat:n4im2lw4n5eejok66c2rkeqvty

Adaptive control for MIMO nonlinear systems based on PID neural networks

Tamer A. Al Zohairy
2016 International Journal Of Engineering And Computer Science  
In this paper a real time control technique for a nonlinear discrete time Multi-input Multi-output systems is presented.  ...  The standard back propagation (BP) algorithm is used to find parameters of the PID neural network controller. The suggested technique modifies the architecture used in SISO to fit with MIMO systems.  ...  neural network for complex nonlinear system control", 2014.  ... 
doi:10.18535/ijecs/v5i8.44 fatcat:3y7b7qkd5jb5fhgm2d5xt4gi64

Design of Self-Constructing Recurrent-Neural-Network-Based Adaptive Control [chapter]

Chun-Fei Hsu, Chih-Min Li
2008 Recurrent Neural Networks  
This paper proposes a recurrent-neural-network-based adaptive control (RNNAC) method, which combines neural-network-based adaptive control, robust control and self-structuring approach, for a class of  ...  Finally, the proposed RNNAC system is applied to control a nonlinear dynamic system. Simulation results are performed to demonstrate the effectiveness of the proposed design method.  ...  The authors appreciate the partial financial support from the National Science Council of Republic of China under grant NSC 95-2622-E-155-004-CC3.  ... 
doi:10.5772/5538 fatcat:zj2eb6hpjvhhndin5n6rw5dcbq

Nonlinear Analysis of Dynamical Complex Networks

Zidong Wang, Bo Shen, Hongli Dong, Jun Hu
2013 Abstract and Applied Analysis  
Complex networks are composed of a large number of highly interconnected dynamical units and therefore exhibit very complicated dynamics.  ...  This special issue aims to bring together the latest approaches to understanding complex networks from a dynamic system perspective. Topics include, but  ...  Acknowledgments This special issue is a timely reflection of the research progress in the area of nonlinear analysis of dynamical complex networks.  ... 
doi:10.1155/2013/530124 fatcat:k2qk6lytnvdcdlkjq6mpga7pya

Adaptive sliding neural network-based vibration control of a nonlinear quarter car active suspension system with unknown dynamics

Azadeh Ghahremani, Hamid Khaloozadeh, Peyman Ghahremani
2018 Vibroengineering PROCEDIA  
A Multilayer Perceptron (MLP) neural network is adopted to estimate the unknown dynamics of the system.  ...  This study investigates adaptive sliding neural network (NN) control for quarter active suspension system with dynamic uncertainties and road disturbances.  ...  Our proof for utilizing new Adaptive neural networked based vibration control of a nonlinear quarter car model is inspired by [12] .  ... 
doi:10.21595/vp.2018.19871 fatcat:uih2krzvprbwbdm5lho3pb2bxm

Design of New Hybrid Neural Structure for Modeling and Controlling Nonlinear Systems

Ahmed Sabah Al-Araji, Shaymaa Jafe'er Al-Zangana
2019 Engineering Journal  
This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems.  ...  hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems.  ...  neural network structure is used to propose a new hybrid neural network model in order to improve the performance of modeling and controlling of the nonlinear system.  ... 
doi:10.31026/j.eng.2019.02.08 fatcat:wtnuivhuyfh2vjzmb6ssudsr5i


G.P. Liu
2002 IFAC Proceedings Volumes  
This paper is concerned with neural-learning control of nonlinear dynamical systems. A variable neural network is introduced for approximating unknown nonlinearities of dynamical systems.  ...  Based on variable neural networks, adaptive neural control and predictive neural control schemes are studied.  ...  The recent intensively studied neural networks bring a new stage in the development of adaptive control for unknown nonlinear systems.  ... 
doi:10.3182/20020721-6-es-1901.00697 fatcat:gwihwxstzzfqvgl2v3a2ffc7qm
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