Neural controllers trained to satisfy a learned dissipativity inequality are shown to stabilize the closed loop and to solve a constructed infinite-horizon optimal control problem.
Review on deep learning applications in frequency analysis and control of modern power system,
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Learning Neural Controllers with Optimality and Stability Guarantees Using Input-Output Dissipativity
Neural controllers trained to satisfy a learned dissipativity inequality are shown to stabilize the closed loop and to solve a constructed infinite-horizon optimal control problem.