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In this paper an algorithm is given for estimating parameters belonging to known bounded intervals. The parameter estimation scheme eliminates the need for a persistently exciting input in certain adaptive control algorithms.
This paper examines the behaviour of a discrete adaptive control system under the influence of output disturbances and unmodelled dynamics. The anomalies which result when the command input is and is not sufficiently exciting are described for both least squares, LS, and least mean square, LMS, type estimators.
The conventional sequential adaptive procedure for estimating noise covariances and input forcing function has suboptimal performance and potential instability. In this work we present a robust procedure for optimally estimating a polynomial-form input forcing function, its time of occurrence and the measurement error covariance matrix, R. This procedure is based on a running window robust regression...
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