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ESO-Based Data-Driven Nonlinear Learning Control System

Posted on:2024-02-28Degree:MasterType:Thesis
Country:ChinaCandidate:X L GuoFull Text:PDF
GTID:2568307142457794Subject:Control Science and Engineering
Abstract/Summary:PDF Full Text Request
For discrete-time nonlinear nonaffine systems,a series of ESO-based data-driven nonlinear learning control methods are proposed.At the same time,the quantitative control problem in networked control is studied.For discrete-time nonlinear nonaffine systems,ESO-based partial form model-free adaptive control method and ESO-based full form model-free adaptive control method are proposed.Firstly,the nonlinear system is equivalently transformed into a modified partial form and a full form linear data model by using the dynamic linearization method.On this basis,a multiple parameters-based adaptive parameter estimation algorithm and a linear ESO are designed to estimate the gradient parameters and extended states of the modified linear data model.The designed control method uses more input and output information of the previous time,which reduces the complexity of the gradient parameter estimation process and improves the control accuracy and robustness of the system.Under the data-driven framework,two control strategies are designed and analyzed without any model information.For repetitive nonlinear nonaffine systems,an ESO-based model-free adaptive iterative learning control method is proposed.Firstly,the repetitive nonlinear system is transformed into an equivalent iterative linear data model by means of the dynamic linearization method along the iteration axis.Then,an iterative parameter update law and an iterative ESO are designed to estimate the unknown parameters and extended states in the iterative linear data model.The proposed method uses more batch input information to update the controller,which can achieve more accurate control effect.Moreover,an assumption existed in the traditional iterative learning control that the initial states are the same and eliminated.Meanwhile,due to the introduction of iterative ESO,the presented scheme has a stronger ability to deal with nonrepetitive uncertainty.For repetitive nonlinear nonaffine systems,in order to solve the problem of limited bandwidth in networked control,ESO-based error quantization model-free adaptive iterative learning control method and ESO-based output quantization model-free adaptive iterative learning control method are considered.Firstly,the repeated nonlinear system is transformed into an equivalent iterative linear data model based on a dynamic linearization method along the iteration axis.Then,the quantization information-based iterative parameter update law,iterative ESO and iterative learning algorithm are designed under the scenarios of error information quantification and output information quantification,respectively.The proposed two approaches avoid the assumption of the same initial condition in the traditional quantized iterative learning control,and solve the problem of reset error in practical applications.
Keywords/Search Tags:nonlinear nonaffine systems, ESO, data-driven control, iterative learning control, data quantization
PDF Full Text Request
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