TABLE OF CONTENTS Parameter estimation of unstable systems is necessary in applications involving adaptive control of processes, satellite launch vehicles or unstable aircraft operating in closed loop. In these applications, under normal conditions, the system operates with the feedback controller and generates controlled responses. The system could become unstable due to sensor failures of critical sensors generating the feedback signals or sudden/unforeseen large dynamic changes in the system. Under these conditions, analysis of the data would give clues to the cause of the failure. This knowledge can be utilised for reconfiguration of the control laws for the systems.
In many applications, it is required to estimate the parameters of the open loop plant from data generated when the system is operating in closed loop. When data for system identification purposes are generated with a dynamic system operating in closed loop, the feedback causes correlations between the input and output variables [1]. This data correlation causes identifiability problems, which result in inaccurate parameter estimates. For estimation of parameters from measured input-output data, it is mandatory that the measured data contain adequate information about the modes of the system being identified. In the case of augmented systems, the measured responses may not display the modes of the system adequately since the feedback is meant to generate controlled responses. It may not be always possible to recover accurately the open loop system dynamics from the identification using closed loop data when conventional approaches of parameter estimation are used. Although some of the conventional parameter...
