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Stability analysis of the RBF-ARX model based nonlinear predictive control

H. Peng, T. Ozaki, K. Nakano, V. Haggan-Ozaki, Y. Toyoda
2003 2003 European Control Conference (ECC)   unpublished
This paper gives stability analysis of the nonlinear predictive control strategy based on the off-line identified RBF-ARX model which is a pseudo-linear time-varying ARX model with system working-point  ...  Stability analysis of the nonlinear predictive controller is given both in unconstrained case and in case of a posterior input constraint.  ...  Stability analysis In this paper, we give the stability analysis of the off-line estimated RBF-ARX model-based nonlinear predictive control strategy presented in Section 2.  ... 
doi:10.23919/ecc.2003.7086520 fatcat:3e6fejmd7fahbl4a5gtwkrrsqm

Event-Triggered-Based External Consensus Protocol of RBF-ARX-Model-Based Networked Multiagent Systems with Nonlinear Dynamics and Communication Delays

Qi Lei, Ying Luo, Sundarapandian Vaidyanathan
2021 Discrete Dynamics in Nature and Society  
By utilizing the RBF-ARX model, the locally linearized time series model can be obtained to describe the behaviour of agents with nonlinear characteristics.  ...  An RBF-ARX modelling method is adopted to approximate a nonlinear system.  ...  Control protocol (16) can achieve consensus of nonlinear multiagent systems due to the RBF-ARX-model-based prediction strategy. Remark 6.  ... 
doi:10.1155/2021/5530878 fatcat:p2nxx3l4ozcdbiel6rycmydwke

Robust Predictive Control Algorithm Based on Parameter Variation Rate Information of Functional-coefficient ARX Model

Feng Zhou, Hui Peng, Gang-Lin Zhang, Xiao-Yong Zeng, Xiao-Yan Peng
2019 IEEE Access  
In this paper, a robust predictive control (RPC) algorithm based on the parameter variation rate information of the RBF-ARX model is proposed.  ...  the RBF-ARX model to improve step response control performance and anti-jamming performance.  ...  COMPARISON AND ANALYSIS OF CONTROL RESULTS In this subsection, the simulation study on the proposed RPC strategy that is based on the parameter variation rate information of the RBF-ARX model (RBF-ARX-RPC  ... 
doi:10.1109/access.2019.2901767 fatcat:265fhvqdwze2xhzsbbdmfabzca

Modelling and Control Approach for Dual Clutch Transmission Vehicles Starting Process based on State-Dependent Autoregressive with Exogenous Model

Yang Yang, Mengmeng Wang, Fugen Xia, Datong Qin, Jihao Feng
2020 IEEE Access  
The validity of this modelling approach is verified via a real vehicle test. On this basis, a nonlinear predictive controller based on SD-ARX model is designed.  ...  INDEX TERMS Starting control, radial basis function (RBF) networks, data-driven, state-dependent autoregressive with exogenous (SD-ARX) model, predictive control, nonlinear system.  ...  Recently, an offline-identified RBF-ARX model-based predictive control has been applied in some real industrial systems; the satisfactory nonlinear modeling accuracy and significant effectiveness of the  ... 
doi:10.1109/access.2020.3014162 fatcat:gcsipnd4fngypg6ewx5wwbmy2e

A Quasi-ARX Model for Multivariable Decoupling Control of Nonlinear MIMO System

Lan Wang, Yu Cheng, Jinglu Hu
2012 Mathematical Problems in Engineering  
Then an adaptive control algorithm is presented using the MIMO quasi-ARX radial basis function network (RBFN) prediction model and some stability analysis of control system is shown.  ...  The proposed controller consists of a traditional PID controller with a decoupling compensator and a feed-forward compensator for the nonlinear dynamics based on the MIMO quasi-ARX model.  ...  Then an adaptive controller is presented using the MIMO quasi-ARX RBFN prediction model. The parameters of such controller are selected based on the generalized minimum control variance.  ... 
doi:10.1155/2012/570498 fatcat:qbebmpc26vb3fiv6p6svm6kgcu

A Two-Stage Scheduling RPC Based on Time-Varying Coefficient Information of State-Dependent ARX Model

Feng Zhou, Peidong Zhu, Minghua Xie, Jun Wu, Lihua Cao
2020 Mathematical Problems in Engineering  
A two-stage scheduling robust predictive control (RPC) algorithm, which is based on the time-varying coefficient information of the state-dependent ARX (SD-ARX) model, is designed for the output tracking  ...  control of a class of nonlinear systems.  ...  information is never used in the designing process of the robust predictive controller. e SD-ARX model is a quasi-LPV model with functional coefficients and is often used in nonlinear system modeling  ... 
doi:10.1155/2020/5319408 fatcat:bk7lukzq3zg6hdhku46y7d5m6q

An RBF-ARX Model-Based Variable-Gain Feedback RMPC Algorithm

Feng Zhou, Peidong Zhu, Yemin Qin, Yu Zheng
2020 IEEE Access  
The RBF-ARX model has been used intensively in modeling and control of nonlinear systems, in which the coefficients of the NARX model are approximated with RBF networks.  ...  In this paper, motivated by the fact that the state feedback control policy with variable-gain feedback can support more freedom for the design of RMPCs, we propose an RBF-ARX model-based variable-gain  ...  The RBF-ARX model [23] has been used intensively in modeling and control of the nonlinear system, in which the coefficients of the NARX model are approximated with RBF networks [24] .  ... 
doi:10.1109/access.2020.2999621 fatcat:5c74zpr2ejed3dw7lv2b6qyzky

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  
System identification is a method of finding the mathematical model of the dynamics system using the input-output data measurement.  ...  This paper gives an overview of the characteristic of the quadcopter and presents a comprehensive survey of the modeling techniques used to determine the flight dynamics of a quadrotor with a particular  ...  Acknowledgement This research is support by Universiti Tun Hussien Onn Malaysia under the Geran Penyelidikan Pascasiswazah (GPPS) Vot H364  ... 
doi:10.30880/ijie.2020.02.01.003 fatcat:gzabba3wl5cafh45qc7fvfc2re

A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study

A.A. Bachnas, R. Tóth, J.H.A. Ludlage, A. Mesbah
2014 Journal of Process Control  
Yet, obtaining accurate models to describe the inherently nonlinear, time-varying dynamics of chemical processes remains a challenge in most model-based control applications.  ...  Model-based control strategies are widely used for optimal operation of chemical processes to respond to the increasing performance demands in the chemical industry.  ...  Acknowledgements The authors thank Dr. Dario Piga for the fruitful discussions as well as his advice on software implementation of the LPV identification approaches.  ... 
doi:10.1016/j.jprocont.2014.01.015 fatcat:rp6wqefzpvfkvfjf3bikfu2hqa

Measurement-based frequency dynamic response estimation using geometric template matching and recurrent artificial neural network

Feifei Bai, Xiaoru Wang, Yilu Liu, Xinyu Liu, Yue Xiang, Yong Liu
2016 CSEE Journal of Power and Energy Systems  
Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications.  ...  In order to find the best input features of the RBF-ANN model, geometric template matching (GeTeM) and quality-threshold (QT) clustering are employed from the time series analysis to compute the similarity  ...  ACKNOWLEDGMENT The authors gratefully acknowledge the FNET team at the University of Tennessee for providing the phasor measurement data from FNET/Grideye for the verification work.  ... 
doi:10.17775/cseejpes.2016.00030 fatcat:rtoxpjppivd3bfpxltdidx2fwq

On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

Tohid Alizadeh, Karim Salahshoor, Mohammad Reza Jafari, Abdollah Alizadeh, Mehdi Gholami
2007 2007 IEEE Conference on Emerging Technologies & Factory Automation (EFTA 2007)  
The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems.  ...  On-line identification of hybrid systems using an adaptive growing and pruning RBF neural Abstract This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for  ...  Acknowledgments The authors would like to thank Dr N. Messai (CReSTIC, Université de Reims Champagne-Ardenne,) for providing the data used for identification in Section 3.1.  ... 
doi:10.1109/efta.2007.4416777 dblp:conf/etfa/AlizadehSJAG07 fatcat:ib4ymgxl6nfyde4wxdpxptkfv4

Defining and applying prediction performance metrics on a recurrent NARX time series model

Ryad Zemouri, Rafael Gouriveau, Noureddine Zerhouni
2010 Neurocomputing  
The approach is based on a recurrent NARX model obtained by linear combination of a recurrent neural network (RNN) output and the real data output.  ...  Nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been successfully demonstrated for modeling the input-output behavior of many complex systems.  ...  The RBF network is commonly used for the purpose of modeling uncertain and nonlinear functions.  ... 
doi:10.1016/j.neucom.2010.06.005 fatcat:fbuqeezdvrhxnflvejrm7asvhm

Towards Accurate and Reproducible Predictions for Prognostic: an Approach Combining a RRBF Network and an AutoRegressive Model

Ryad Zemouri, Rafael Gouriveau
2010 IFAC Proceedings Volumes  
In prognostic's field, the lack of knowledge on the behavior of equipments can impede the development of classical dependability analysis, or the building of effective physic-based models.  ...  : the ARX attempts to correct the error of predictions of the RRBF.  ...  In this context, the purpose of the work is to propose a nonlinear forecasting model based on ANN and to improve its prediction performances.  ... 
doi:10.3182/20100701-2-pt-4012.00025 fatcat:c2pmflp54bdqnd42bkjcijpzau

Identification and Control of Dynamical Systems Using the Self-Organizing Map

G.A. Barreto, A.F.R. Araujo
2004 IEEE Transactions on Neural Networks  
The performance of the proposed approach is evaluated on a variety of complex tasks, namely: (i) time series prediction, (ii) identification of SISO/MIMO systems, and (iii) nonlinear predictive control  ...  models for dynamical system identification and control.  ...  Acknowledgements The authors thank FAPESP (98/12699-7) and CNPQ (DCR: 305275/2002-0) for the financial support.  ... 
doi:10.1109/tnn.2004.832825 pmid:18238091 fatcat:6rtbjnj6dfdm3fdvxbqmuvompa

Robust model-based fault diagnosis for air handling units

Timothy Mulumba, Afshin Afshari, Ke Yan, Wen Shen, Leslie K. Norford
2015 Energy and Buildings  
The estimator presumes an autoregressive time series model with exogenous variables (ARX).  ...  In this paper, a model-based fault diagnosis method is developed by applying support vector machine (SVM) techniques to model parameters recursively calculated by an online estimator.  ...  Acknowledgments This research was supported by the Executive Affairs Authority (EAA) of Abu Dhabi, United Arab Emirates as part of the project Predictive Maintenance: Fault Detection and Diagnosis of AC  ... 
doi:10.1016/j.enbuild.2014.10.069 fatcat:hjduwmtlwre5lcmfu5upoel3qa
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