Abstract
We propose an active weighting algorithm for composite adaptive control to reduce the state and estimate errors while maintaining the estimation quality. Unlike previous studies that construct the composite term by simply stacking, removing, and pausing observed data, the proposed method efficiently utilizes the data by providing a theoretical set of weights for observations that can actively manipulate the composite term to have desired characteristics. As an example, a convex optimization formulation is provided, which maximizes the minimum eigenvalue while keeping other constraints, and an illustrative numerical simulation is also presented.
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