Design and convergence of iterative learning control based on neural networks
The purpose of this study is to develop an effective approach to control design for repetitive nonlinear processes based on the iterative learning control technique. The idea adopted here to circumvent the inherent uncertainty of the controlled system modelling is to enhance the iterative learning scheme with neural networks used for controller design and error estimation. In result, we obtain the iterative control update rule, which can be calculated based on efficient data-driven technique for training neural network. Finally, the proposed approach is illustrated on the application example to nonlinear pneumatic servomechanism.
We consider asymptotic behavior of partial sums and sample covariances for linear processes whose innovations are dependent. Central limit theorems and invariance principles are established under fairly mild conditions. Our results go beyond earlier ones by allowing a quite wide class of innovations which includes many important nonlinear time series models. Applications to linear processes with GARCH innovations and other nonlinear time series models are discussed.