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Learning from similar systems and online data-driven LQR using iterative randomised data compression
The first instance of this input is used to actuate the current system and the corresponding instantaneous output is used to iteratively re-solve the LQR problem through a computationally inexpensive randomized rank-one update of the old compressed data.
Fast Randomized Subspace System Identification for Large I/O Data
The algorithm is especially well-suited to identify high-order and multi-scale systems with both fast and slow dynamics, which typically require a large number of input-output data samples for accurate identification using traditional subspace methods.
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