Identification of Non-Linear Multi-Variable Systems Subjected to Random Input Signals

In the optimal control of any physical process about its operating point, a requirement of prime importance is the identification of the process parameters. This requires the realisation of both the static and dynamic characteristics of the control system. A method of identifying multi-variable non-linear systems is presented in this paper. The method uses correlation techniques around the tracked operating point of the system. The correlation method uses several uncorrelated maximal length (pseudo-random) sequences of small amplitude as test signals. This facilitates on-line identification during the normal process operation. A further advantage of this method is that a system identification may be made in the presence of noise and other inherent disturbances. The technique is first applied to a multi-variable system containing cubic non-linearities and then to one containing four hysteresis elements. Finally, an identification was made of the non-linear cross-coupling dynamics between the roll and yaw axes of an aircraft.

Abstract licence: CC BY

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