IL-based double close loop modelling and control for SDC systems

A double closed loop stochastic distribution modelling and control structure based on iterative learning (IL) is presented for non-Gaussian dynamical stochastic systems in this paper. Each of the outer loop and the inner loop iteration are called as BATCH and batch, respectively. The output probability density functions (PDFs) of the system are approximated by radial basis function neural network (RBFNN). Iterative learning method is applied to adjust the parameters (i.e. the centers and widths of RBFs ) of the RBFNN, and then a standard state-space model is constructed within each BATCH by the use of subspace method. Application the state-space model, an IL-based controller, which tunes the control input signals in terms of the shaping tracking error from last batch, is given in the inner loop of the system. A simulation case study is included to show the effectiveness of the proposed algorithm and encouraging results have been obtained.