Normalized Learning Rule for Iterative Learning Control

You, Byungyong

doi:10.1007/s12555-017-0194-z

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…Unlike many works on ILC (for example, Jin and Xu, 2013; Ding and Yang, 2014; Jin and Xu, 2013; Ji et al, 2016; Li and Shen, 2017; You, 2018), in which a prior knowledge about the control direction must be available to design a controller, In this paper, we do not need any prior knowledge of the control directions. Thus, the Nussbaum-type function is used to overcome this problem.…”

Section: Resultsmentioning

confidence: 99%

“…In control of systems, it is more important and practical to design a control law with less prior knowledge of the nonlinear systems. However, in most works on ILC, a prior knowledge about the control direction must be available to design a controller (see, for instance, Ding and Yang, 2014; Jin and Xu, 2013; Ji et al, 2016; Li and Shen, 2017; You, 2018). Without this assumption, the stability proof of the closed loop systems becomes more difficult.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Iterative learning radial basis function neural networks control for unknown multi input multi output nonlinear systems with unknown control direction

Bensidhoum

Bouakrif

Zasadzinski

2019

Transactions of the Institute of Measurement and Control

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In this paper, an iterative learning radial basis function neural-networks (RBF NN) control algorithm is developed for a class of unknown multi input multi output (MIMO) nonlinear systems with unknown control directions. The proposed control scheme is very simple in the sense that we use just a P-type iterative learning control (ILC) updating law in which an RBF neural network term is added to approximate the unknown nonlinear function, and an adaptive law for the weights of RBF neural network is proposed. We chose the RBF NN because it has universal approximation capabilities and can approximate any continuous function. In addition, among the advantages of our controller scheme is the fact that it is applicable to deal with a class of nonlinear systems without the need to satisfy the global Lipschitz continuity condition and we assume, only, that the unstructured uncertainty is norm-bounded by an unknown function. Another advantage of the proposed controller and unlike other works on ILC, we do not need any prior knowledge of the control directions for MIMO nonlinear system. Thus, the Nussbaum-type function is used to solve the problem of unknown control directions. In order to prove the asymptotic stability of the closed-loop system, a Lyapunov-like positive definite sequence is used, which is shown to be monotonically decreasing under the control design scheme. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed control scheme.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative learning radial basis function neural networks control for unknown multi input multi output nonlinear systems with unknown control direction

Bensidhoum

Bouakrif

Zasadzinski

2019

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

Design of the Linear Quadratic Structure Based Predictive Functional Control for Industrial Processes Against Partial Actuator Failures Using GA Optimization

Zou

Wang

2019

Int. J. Control Autom. Syst.

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This paper addresses the genetic algorithm (GA) optimization and the linear quadratic (LQ) structure based predictive functional control (PFC) for batch processes under non-repetitive unknown disturbances and partial actuator faults. First, by adopting the extended non-minimal state space (ENMSS) model in which the state variables and the tracking error are united, the new state vector with more degrees is provided for the controller design. In order to enhance the ensemble control performance under the PFC structure, GA is adopted for the optimization of the weighting matrix in the controller. The case study on the injection velocity control in an injection molding machine demonstrates the effectiveness of the proposed PFC scheme against various disadvantages.

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New MIT Control Strategy Combined with Iterative Learning Control

Song

Jingzhuo

2022

Int. J. Control Autom. Syst.

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Normalized Learning Rule for Iterative Learning Control

Cited by 5 publications

References 20 publications

Iterative learning radial basis function neural networks control for unknown multi input multi output nonlinear systems with unknown control direction

Iterative learning radial basis function neural networks control for unknown multi input multi output nonlinear systems with unknown control direction

Design of the Linear Quadratic Structure Based Predictive Functional Control for Industrial Processes Against Partial Actuator Failures Using GA Optimization

New MIT Control Strategy Combined with Iterative Learning Control

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