A least-squares based model-fitting identification technique for diesel prime-movers with unknown dead-time

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Abstract
The recursive least-squares estimation technique is extended for application to systems with small but unknown dead-time delays. This problem is studied with particular reference to when the diesel engine is used as a prime-mover, in which case the dead-time leads to a significant variation in dynamic performance. In practice, least-squares estimators are found to have slow convergence and large output errors, when applied to systems with time-delays. It is shown that by a suitably constrained algorithm, convergence can be quickened and output error can be kept within acceptable limits. Low-order system models with changing dead-time are in general, characterized by modeling errors that are to an extent correlated with the input control signal to the plant. Despite this fact, the developed approach yields very small prediction errors, even when large deterministic disturbances are present. This suggests its possible use in conjunction with adaptive controllers of various types.