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PROJECT TITLE :

Randomized Control Strategies Under Arbitrary External Noise

ABSTRACT:

This technical note deals with the identification drawback for a linear dynamic plant described by an autoregressive moving average model with additive external noise (exogenous disturbance). We have a tendency to use an approach which is based on randomization of control and permits to make minimal assumptions regarding the noise: randomized test perturbations in control and also the external noise must be stochastically independent. In particular, any unknown however bounded deterministic real sequence is an example of such a noise. In the case of a finite set of observations, we have a tendency to propose 2 procedures for computing information-based confidence regions for unknown parameters of the plant. They may be employed in adaptive management schemes. The first procedure is of the stochastic approximation kind, while the second is developed in the final framework of “counting of leave-out sign-dominant correlation regions” (LSCR), which returns confidence regions that are absolute to contain the true parameters with a prescribed chance. If the amount of observations increases infinitely, we propose the combined procedure for computing confidence regions which shrink to the true parameters asymptotically. The theoretical results are illustrated via a simulation example with a nonminimum-part second-order plant.


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