Derives an ε-local minimax excess-cost lower bound for learning LQG controllers from offline trajectories of a linear exploration policy, expressed via the Hessian of the LQG cost and inverse Fisher information, and instantiates it on fragile robust-control examples.
From experiment design to closed-loop control,
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Row-norm-minimizing right inverse via SOCP plus A-optimal input design within the constrained matrix zonotope framework reduces conservatism in data-driven reachable sets for linear and piecewise-affine systems.
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The Fragility of Learning LQG Controllers
Derives an ε-local minimax excess-cost lower bound for learning LQG controllers from offline trajectories of a linear exploration policy, expressed via the Hessian of the LQG cost and inverse Fisher information, and instantiates it on fragile robust-control examples.
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Data-Driven Reachability Analysis with Optimal Input Design
Row-norm-minimizing right inverse via SOCP plus A-optimal input design within the constrained matrix zonotope framework reduces conservatism in data-driven reachable sets for linear and piecewise-affine systems.