State-tracking control of non-linear systems with unknown dynamics in the presence of disturbances

This paper deals with the design of a state tracking controller for a non-linear dynamical system where the dynamics are only partly known and where only part of the state vector components are directly measured. The design procedure takes the influence of state disturbances and measurement noise into account, which are naturally present in practical designs of control systems. The design of a non-linear controller is based on the estimation of a simulation model of the process in state space form. A non-linear filter gain is estimated to build up a state observer to be used for the optimization of a non-linear static state feedback controller. All non-linear maps are approximated by neural networks trained by a mixed stochastic deterministic optimization procedure. The performance of the proposed design procedure is demonstrated by means of at simulation example