Neural-Based Iterative Learning Control

Choi, Jin Young; Choi, Chong‐Ho; Park, Hyun Joo

doi:10.1007/978-1-4615-5629-9_12

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Implementatio N Of the Learning Controller Using A Fuzzy Net1

Introduction1

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Cited by 3 publications

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“…It is not clearly addressed what information of input± output coupling matrix is needed for the design of learning gain in most of the works. Motivated by the recent work (Choi and Park 1997), one can easily ® nd that the best choice of L i for rapid convergence is to set…”

Section: Implementatio N Of the Learning Controller Using A Fuzzy Netmentioning

confidence: 99%

A sampled-data iterative learning control using fuzzy network design

Chien

2000

International Journal of Control

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Section: Implementatio N Of the Learning Controller Using A Fuzzy Netmentioning

confidence: 99%

A sampled-data iterative learning control using fuzzy network design

Chien

2000

International Journal of Control

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“…In general, it is hard to design the learning gain if the nonlinear dynamic plant is highly nonlinear and unknown. In order to get the input/output coupling function (matrix), the ILC using a neural or fuzzy system to solve the learning gain implementation problem can be found in [8,9]. A neural network or a fuzzy system was used to approximate the inverse of plant's input/output coupling function (matrix).…”

Section: Introductionmentioning

confidence: 99%

An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants

Wang

Chien

2012

Journal of Control Science and Engineering

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We present a design method for iterative learning control system by using an output recurrent neural network (ORNN). Two ORNNs are employed to design the learning control structure. The first ORNN, which is called the output recurrent neural controller (ORNC), is used as an iterative learning controller to achieve the learning control objective. To guarantee the convergence of learning error, some information of plant sensitivity is required to design a suitable adaptive law for the ORNC. Hence, a second ORNN, which is called the output recurrent neural identifier (ORNI), is used as an identifier to provide the required information. All the weights of ORNC and ORNI will be tuned during the control iteration and identification process, respectively, in order to achieve a desired learning performance. The adaptive laws for the weights of ORNC and ORNI and the analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will asymptotically converge to zero and repetitive output tracking error will asymptotically converge to zero except the initial resetting error.

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