An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants
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 contro...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2012/545731 |
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| _version_ | 1832550734572290048 |
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| author | Ying-Chung Wang Chiang-Ju Chien |
| author_facet | Ying-Chung Wang Chiang-Ju Chien |
| author_sort | Ying-Chung Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e7839f273af34bd19a023ec018b4b0bd |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-e7839f273af34bd19a023ec018b4b0bd2025-02-03T06:05:58ZengWileyJournal of Control Science and Engineering1687-52491687-52572012-01-01201210.1155/2012/545731545731An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic PlantsYing-Chung Wang0Chiang-Ju Chien1Department of Electronic Engineering, Huafan University, Shihding, New Taipei City 223, TaiwanDepartment of Electronic Engineering, Huafan University, Shihding, New Taipei City 223, TaiwanWe 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.http://dx.doi.org/10.1155/2012/545731 |
| spellingShingle | Ying-Chung Wang Chiang-Ju Chien An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants Journal of Control Science and Engineering |
| title | An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants |
| title_full | An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants |
| title_fullStr | An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants |
| title_full_unstemmed | An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants |
| title_short | An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants |
| title_sort | output recurrent neural network based iterative learning control for unknown nonlinear dynamic plants |
| url | http://dx.doi.org/10.1155/2012/545731 |
| work_keys_str_mv | AT yingchungwang anoutputrecurrentneuralnetworkbasediterativelearningcontrolforunknownnonlineardynamicplants AT chiangjuchien anoutputrecurrentneuralnetworkbasediterativelearningcontrolforunknownnonlineardynamicplants AT yingchungwang outputrecurrentneuralnetworkbasediterativelearningcontrolforunknownnonlineardynamicplants AT chiangjuchien outputrecurrentneuralnetworkbasediterativelearningcontrolforunknownnonlineardynamicplants |