Intelligent model reference adaptive distribution control for non-Gaussian stochastic systems

This paper presents model reference adaptive control (MRAC) approach to control the shape of output distribution in non-Gaussian stochastic systems. The method is based on Iterative Learning Control (ILC) and employs a neural network framework for controller design. The output probability density function (PDF) tracking problem is first reduced to dynamic neural network (NN) weight control. It is assumed that the dynamic behaviour of such weights is nonlinear and unknown. To apply the ILC-based tuning, the control horizon is split up to certain number of intervals hereinafter called batches. The proposed ILC method is comprised of two main modes, namely within each batch and between any two adjacent batches, and includes three stages; (a) NN-based nonlinear dynamic system identification ( b) MRAC of the weight control loop within each batch, and (c) Tuning the RBF centers, widths, and controller neural network parameters between any two adjacent batches. Simulation results confirm the effectiveness of the method.