This paper investigates an event-triggered model-free adaptive control for nonaffined nonlinear systems under a data-driven design framework. By introducing compact form dynamic linearization (CFDL) scheme, linear data model of the nonaffine system is derived. Then, parameter estimation algorithm developed to offline identify model. On basis identified model, CFDL-based (CFDL-ET-MFAC) by designing event-triggering condition guarantee Lyapunov stability. The action active only when satisfied....

An event-triggered nonlinear iterative learning control (ET-NILC) method is presented for repetitive nonaffine and systems that have 2-D dynamic behavior along both time iteration directions. Based on the virtual linear data model, ET-NILC proposed by designing an event triggering condition based Lyapunov-like stability analysis conducted direction. The gain function of updated parameter estimation law to enhance robustness. From perspective dynamics, a feedforward event-triggering can be...

In this work, a data-driven indirect iterative learning control (DD-iILC) is presented for repetitive nonlinear system by taking proportional-integral-derivative (PID) feedback in the inner loop. A linear parametric tuning algorithm set-point developed from an ideal function that exists theory utilizing dynamic linearization (IDL) technique. Then, adaptive updating strategy of parameter law optimizing objective controlled system. Since considered and nonaffine with no available model...

In this article, the optimal consensus problem at specified data points is considered for heterogeneous networked agents with iteration-switching topologies. A point-to-point linear model (PTP-LDM) proposed to establish an iterative input-output relationship of between two consecutive iterations. The PTP-LDM only used facilitate subsequent controller design and analysis. sequel, identification algorithm presented estimate unknown parameters in PTP-LDM. Next, event-triggered learning control...

In this work, a data-driven virtual reference setting learning (DDVRSL) method is proposed to enhance the proportional-derivative (PD) feedback controller of repetitive nonlinear system. First, an ideal law presented in outer loop control system tune setting. Such exists theoretically and transferred linear parametric DDVRSL via iterative dynamic linearization (IDL). Next, adaptation for estimation parameters subject which also into form by using IDL method. The algorithm tunes gains input...

Abstract Model‐free adaptive control (MFAC) is an effective data‐driven method to deal with nonlinear and nonaffine systems. In this article, a set‐point tuning (DDST) approach proposed for MFAC enhance its performance. The based (DDST‐MFAC) system consists of two loops. inner loop takes the as feedback controller where virtual reference error signal adopted in input. DDST outer derived from ideal (NST) law, which exists theory, meet target. To realize theoretically existing NST, dynamic...

This paper reconsiders the iterative learning control (ILC) problem for variable trial lengths via compensating output data by using an auxiliary predictive model when controlled process does not reach desired length. Moreover, this aims to propose a general and data-driven ILC method without requiring any explicit mechanistic information. Specifically, difference with state transition expression is performed at first over length in iteration domain build input-output dynamics of linear...

In this paper, an improved high-order model free adaptive control (IHOMFAC) method is proposed. Considering more information of the previous time, a parameter estimation algorithm designed, which different from traditional MFAC. The design based on principle symmetric similarity, not only considers knowledge time in law, but also uses algorithm, conducive to enhancing performance. Simulation results declare that IHOMFAC has superior performance than

Summary Many practical batch processes operate repetitively in industry and lack intermediate measurements for the interested process variables. Moreover, initial states as well desired product objective often vary with different runs because of existence many uncertainties practice. This work proposes a novel adaptive terminal iterative learning control method to deal random points states. The run‐varying are formulated by stochastic high‐order internal model, which is further incorporated...

Abstract This paper explores the iterative learning control (ILC) problem for two‐dimensional (2D) multi‐input multi‐output nonlinear parametric systems by taking all nonrepetitive uncertainties of stochastic initial shifts, different tracking tasks and nonuniform trial lengths into consideration. A 2D variable is defined with Bernoulli distribution first time to handle iteration‐varying lengths. The desired output incorporated law as a feedback compensate changes tasks. An parameter...

In this paper, a high-order model-free adaptive iterative learning control (HOMFAILC) scheme is proposed for nonlinear non-affine discrete-time systems. The main feature of the HOMFAILC that not only input law designed with higher-order algorithms but also parameter updating has similar forms. Using additional knowledge in both and estimation law, performance can be improved consequently. Furthermore, structure help to achieve better performance. convergence proved rigorous mathematical...

This article aims at solving the problems of data-driven control design in presence strong uncertainties, hard nonlinearities, and model dependency by using a dynamic linearization (DL) method an extended state observer (ESO). An unknown nonlinear nonaffine system is considered, whose input–output dynamics then equivalently reformulated into modified linear data (mLDM) which both parametric increment description that affine to input unmodeled uncertainties along with disturbances are...

In this paper, an iterative learning recursive least squares (ILRLS) identification method is developed by considering a class of repetitive systems. First, discrete-time system corrupted white noise, we present linear time-varying data model to describe the input-output dynamic behavior in iteration domain. On basis, two ILRLS methods are proposed taking both noises and colored into consideration. With extensive analysis, shown applicable nonlinear systems owing their data-driven nature...

In this paper, we propose two data-driven adaptive tuning (DDAT) approaches of iterative learning control (ILC) for nonlinear non-affine systems. First, a compact-form dynamic linearization (CFIDL) method is introduced to transfer the original system into linear data model. Then, design an objective function gains PD-type ILC law. By optimizing designed cost subjected model, CFIDL-based DDAT proposed, where only real I/O are used without requiring any mechanistic model information....

This work aims at presenting a new sampled-data model-free adaptive control (SDMFAC) for continuous-time systems with the explicit use of sampling period and past input output (I/O) data to enhance performance. A sampled-data-based dynamical linearization model (SDDLM) is established address unknown nonlinearities nonaffine structure system, which all complex uncertainties are compressed into parameter gradient vector that further estimated by designing updating law. By virtue SDDLM, we...

Abstract In order to deal with the I/O constraints in a practical plant, soft limiter is often added into control design procedure directly; however, performance of based approach will be degraded greatly due use constraints. This paper proposes data‐driven optimal terminal iterative learning (constraint‐DDOTILC) for end product quality batch processes hard To nonlinearities, novel dynamic linearization method without omitting any information original plant introduced such that derived...