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Stability analysis of the RBF-ARX model based nonlinear predictive control
2003
2003 European Control Conference (ECC)
unpublished
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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
2021
Discrete Dynamics in Nature and Society
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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
2019
IEEE Access
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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
2020
IEEE Access
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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
2012
Mathematical Problems in Engineering
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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
2020
Mathematical Problems in Engineering
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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
2020
IEEE Access
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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
2020
Journal of Advanced Mechanical Engineering Applications
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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
2014
Journal of Process Control
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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
2016
CSEE Journal of Power and Energy Systems
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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
2007
2007 IEEE Conference on Emerging Technologies & Factory Automation (EFTA 2007)
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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
2010
Neurocomputing
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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
2010
IFAC Proceedings Volumes
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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
2004
IEEE Transactions on Neural Networks
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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
2015
Energy and Buildings
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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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