Statistic tracking control: A multi-objective optimization algorithm

Abstract

This paper addresses a new type of control framework for dynamical stochastic systems, which is called statistic tracking control here. General non-Gaussian systems are considered and the tracked objective is the statistic information (including the moments and the entropy) of a given target probability density function (PDF), rather than a deterministic signal. The control is aiming at making the statistic information of the output PDFs to follow those of a target PDF. The B-spline neural network with modelling error is applied to approximate the corresponding dynamic functional. For the nonlinear weighting system with time delays in the presence of exogenous disturbances, the generalized H 2 and H ∞ optimization technique is then used to guarantee the tracking, robustness and transient performance simultaneously in terms of LMI formulations. © Springer-Verlag Berlin Heidelberg 2006.