{"title":"RBF NN observer based adaptive feedback control for the ABS system under parametric uncertainties and modelling errors","authors":"A. Hamou, Rabhi Abdelhamid, Belkheiri Mohammed","doi":"10.1504/IJMIC.2020.10037492","DOIUrl":null,"url":null,"abstract":"An antilock braking (ABS) scheme control is a relatively difficult task due to its uncertain nonlinear dynamics. According to the requirement that the braking process must be fast and robust, we contribute to extending the universal function approximation property of the radial-basis-function (RBF) neural network (NN) to design both: (a) adaptive NN observer to estimate the tracking error dynamics; and (b) intelligent NN output feedback controller (OFC) that will overcome successfully the existing high uncertainties. Notice that the OFC is introduced to linearise the ABS nonlinear system, and the dynamic compensator is involved to stabilise the linearised system. The estimated states are used in the adaptation laws as an error signal. Simulations of the proposed control algorithm based adaptive RBFNN observer are conducted then compared to the Bang-bang controller to demonstrate its practical potential. Furthermore, its efficiency has been successfully confirmed through a robustness test.","PeriodicalId":46456,"journal":{"name":"International Journal of Modelling Identification and Control","volume":"54 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Modelling Identification and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJMIC.2020.10037492","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
An antilock braking (ABS) scheme control is a relatively difficult task due to its uncertain nonlinear dynamics. According to the requirement that the braking process must be fast and robust, we contribute to extending the universal function approximation property of the radial-basis-function (RBF) neural network (NN) to design both: (a) adaptive NN observer to estimate the tracking error dynamics; and (b) intelligent NN output feedback controller (OFC) that will overcome successfully the existing high uncertainties. Notice that the OFC is introduced to linearise the ABS nonlinear system, and the dynamic compensator is involved to stabilise the linearised system. The estimated states are used in the adaptation laws as an error signal. Simulations of the proposed control algorithm based adaptive RBFNN observer are conducted then compared to the Bang-bang controller to demonstrate its practical potential. Furthermore, its efficiency has been successfully confirmed through a robustness test.
期刊介绍:
Most of the research and experiments in the fields of science, engineering, and social studies have spent significant efforts to find rules from various complicated phenomena by observations, recorded data, logic derivations, and so on. The rules are normally summarised as concise and quantitative expressions or “models". “Identification" provides mechanisms to establish the models and “control" provides mechanisms to improve the system (represented by its model) performance. IJMIC is set up to reflect the relevant generic studies in this area.