Learns regionally stable RNN models from input-output data by deriving LMI constraints from generalized sector conditions on deadzone activations and a barrier function to certify forward invariance on a compact set.
European Journal of Control , volume=
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Derives LMI-based stability conditions and peak-to-peak gains for a GPSOL/DERL adaptive controller on first-order SISO nonlinear systems to enforce bounded output errors, verified in simulation and on a pneumatic test rig.
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Learning the dynamics of nonlinear systems with regional stability guarantees through linear matrix inequality constraints
Learns regionally stable RNN models from input-output data by deriving LMI constraints from generalized sector conditions on deadzone activations and a barrier function to certify forward invariance on a compact set.
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Online Learning-Based Control with Guaranteed Error Bounds for a Class of Nonlinear Systems
Derives LMI-based stability conditions and peak-to-peak gains for a GPSOL/DERL adaptive controller on first-order SISO nonlinear systems to enforce bounded output errors, verified in simulation and on a pneumatic test rig.