This paper presents a recursive scheme for optimizing the gain of an exponentiallly weighted moving average (EWMA) controller under stability constraints. The objective is to minimize the asymptotic mean square error in the output with minimal a priori information. The algorithm hinges on a simple representation of the optimal EWMA gain. Both step and drift disturbances are considered. It is shown that the gain sequence generated by the algorithm always yields a stable system. Furthermore, this sequence is shown to converge to a suboptimal value. Extensions to the algorithm to the case where there is model uncertainty are also presented. The algorithm is verified via simulation. Data from a manufacturing implementation is presented.Index Terms-Adaptive optimization, exponentially weighted moving average (EWMA) controller, run-to-run control (R2R), stochastic approximation.

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Image motioncompensation (IMC) can be used to augment conventional line -of -sight (LOS) stabilization systems to improve stabilization performance. The tradeoffs involved in deciding when to use.IMC and the expected performance improvements are reviewed.Several IMC concepts are outlined including the special case of a gimbaled optical element with a one -to -one scale factor between mechanical and image motion. The advantage of this IMC stabilization approach over other IMC techniques is that performance is not limited by the bandwidth of the sensor or actuator.Performance, however, is limited by optical scale factor accuracies. Precision measurement of the gimbal motion is not required to achieve LOS stabilization.The "restored mass stabilization" approach offers performance improvements without the high cost of precision inertial components.

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