Neural network co-design of output-feedback CBF, observer, and controller for partially observed continuous-time systems with input constraints, using augmented-state barrier conditions and a validity condition for safety guarantees beyond training data.
Lipschitz constant estimation for general neural network archi- tectures using control tools
2 Pith papers cite this work. Polarity classification is still indexing.
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LipKernel parameterizes dissipative convolution kernels via 2-D Roesser state-space models so that layer-wise LMIs enforce network Lipschitz bounds while allowing standard fast convolution evaluation after training.
citing papers explorer
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Neural Network-based Co-design of Output-Feedback Control Barrier Function and Observer with Input Constraints
Neural network co-design of output-feedback CBF, observer, and controller for partially observed continuous-time systems with input constraints, using augmented-state barrier conditions and a validity condition for safety guarantees beyond training data.
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LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
LipKernel parameterizes dissipative convolution kernels via 2-D Roesser state-space models so that layer-wise LMIs enforce network Lipschitz bounds while allowing standard fast convolution evaluation after training.