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Found 8 papers, 0 papers with code
Design of Stochastic Quantizers for Privacy Preservation
This insight enables us to use quantization intentionally as a means to achieve the seemingly conflicting two goals of maintaining control performance and preserving privacy at the same time; towards this end, we further investigate a dynamic stochastic quantizer.
Krasovskii Passivity for Sampled-data Stabilization and Output Consensus
In this paper, we establish the novel concept of Krasovskii passivity for sampled discrete-time nonlinear systems, enabling Krasovskii-passivity-based control design under sampling.
An LMI Framework for Contraction-based Nonlinear Control Design by Derivatives of Gaussian Process Regression
Contraction theory formulates the analysis of nonlinear systems in terms of Jacobian matrices.
${\mathcal K}$-monotonicity and feedback synthesis for incrementally stable networks
We discuss the role of monotonicity in enabling numerically tractable modular control design for networked nonlinear systems.
Krasovskii and Shifted Passivity Based Output Consensus
Motivated by current sharing in power networks, we consider a class of output consensus (also called agreement) problems for nonlinear systems, where the consensus value is determined by external disturbances, e. g., power demand.
Learning Stabilizable Deep Dynamics Models
When neural networks are used to model dynamics, properties such as stability of the dynamics are generally not guaranteed.
Controller Reduction for Nonlinear Systems by Generalized Differential Balancing
In this paper, we aim at developing computationally tractable methods for nonlinear model/controller reduction.
Contraction Analysis of Discrete-time Stochastic Systems
In this paper, we develop a novel contraction framework for stability analysis of discrete-time nonlinear systems with parameters following stochastic processes.
