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Towards non-stochastic targeted exploration
We present a novel targeted exploration strategy for linear time-invariant systems without stochastic assumptions on the noise, i. e., without requiring independence or zero mean, allowing for deterministic model misspecifications.
Sequential learning and control: Targeted exploration for robust performance
This provides an a priori upper bound on the remaining model uncertainty after exploration, which can further be leveraged in a gain-scheduling controller design that guarantees robust performance.
Robust Dual Control based on Gain Scheduling
We present a novel strategy for robust dual control of linear time-invariant systems based on gain scheduling with performance guarantees.
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