Dominance functions from a small number of trajectories serve as dissipative and expressive building blocks for formal safety certificates in monotone discrete-time systems.
Modeling nonlinear control systems via
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Regularized DDPC formulations are convex relaxations of bi-level identification-control problems, and the new A-DDPC algorithm outperforms prior regularized methods by lowering bias and variance errors.
Koopman operators provide a global linearization of parameterized nonlinear systems with stable equilibria into finite-dimensional linear systems that depend continuously on the parameter.
citing papers explorer
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Trajectory-based Safety of Monotone Systems: Verification and Control Synthesis
Dominance functions from a small number of trajectories serve as dissipative and expressive building blocks for formal safety certificates in monotone discrete-time systems.
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Regularization in Data-driven Predictive Control: A Convex Relaxation Perspective
Regularized DDPC formulations are convex relaxations of bi-level identification-control problems, and the new A-DDPC algorithm outperforms prior regularized methods by lowering bias and variance errors.
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Global Linearization of Parameterized Nonlinear Systems with Stable Equilibrium Point Using the Koopman Operator
Koopman operators provide a global linearization of parameterized nonlinear systems with stable equilibria into finite-dimensional linear systems that depend continuously on the parameter.