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