An output-recurrent-neural-network-based iterative learning control for unknown nonlinear dynamic plants. (English) Zbl 1235.93270
Summary: 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.
MSC:
| 93E35 | Stochastic learning and adaptive control |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
| 68T05 | Learning and adaptive systems in artificial intelligence |
Keywords:
iterative learning control system; output recurrent neural network (ORNN); output recurrent neural identifier (ORNI); adaptive laws; Lyapunov like analysisReferences:
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