Noise control, dynamic optimization

A comprehensive framework of robust feedback control of combustion instabilities in propulsion systems has been established. The model appears to be the most complete of its kind to date, and accommodates various unique phenomena commonly observed in practical combustion devices. Several important aspects of distributed control process (including time delay, plant disturbance, sensor noise, model uncertainty, and performance specification) are treated systematically, with emphasis placed on the optimization of control robustness and system performance. In addition, a robust observer is established to estimate the instantaneous plant dynamics and consequently to determine control gains. Implementation of the controller in a generic dump combustor has been successfully demonstrated. 

In this review, we have expounded our universal approach to the dynamical control of qubits subject to noise or decoherence. It is based on a general non-Markovian ME valid for weak System-bath coupling and arbitrary modulations, since it does not invoke the RWA. The resulting universal convolution formula provide intuitive clues as to the optimal tailoring of modulation and noise spectra. 

We emphasize that the question of stability of a CA under small random perturbations is in itself an important unsolved problem in the theory of fluctuations [92-94] and the difficulties in solving it are similar to those mentioned above. Thus it is unclear at first glance how an analogy between these two unsolved problems could be of any help. However, as already noted above, the new method for statistical analysis of fluctuational trajectories [60,62,95,112] based on the prehistory probability distribution allows direct experimental insight into the almost deterministic dynamics of fluctuations in the limit of small noise intensity. Using this techique, it turns out to be possible to verify experimentally the existence of a unique solution, to identify the boundary condition on a CA, and to find an accurate approximation of the optimal control function. 

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