Search Results for author:
Found 15 papers, 1 papers with code
Reduced-order Modeling of Modular, Position-dependent Systems with Translating Interfaces
In this paper, a modular model framework is proposed, which allows to construct an interconnected system model, which captures the position-dependent behavior of systems with translating interfaces, such as linear guide rails, through a position-dependent interconnection structure.
Modular Redesign of Mechatronic Systems: Formulation of Module Specifications Guaranteeing System Dynamics Specifications
To address this challenge, a modular redesign framework is proposed in this paper.
Passivity-Preserving, Balancing-Based Model Reduction for Interconnected Systems
This paper proposes a balancing-based model reduction approach for an interconnection of passive dynamic subsystems.
Uncertainty Learning for LTI Systems with Stability Guarantees
We propose a methodology to extend the dynamics of an LTI (without uncertainty) with an uncertainty model, based on measured data, to improve the predictive capacity of the model in the input-output sense.
Mode Selection for Component Mode Synthesis with Guaranteed Assembly Accuracy
The standard approach to component mode synthesis for such system is to select the eigenmodes of a component that are most important to accurately model the dynamic behavior of this component in a certain frequency range of interest.
Robust Fault Estimators for Nonlinear Systems: An Ultra-Local Model Design
We provide sufficient conditions that guarantee stability of the estimation error dynamics: firstly, asymptotic stability (i. e., perfect fault estimation) in the absence of perturbations induced by fault model mismatch (mismatch between internal, ultralocal model for the fault and the actual fault characteristics), uncertainty, external disturbances, and measurement noise and, secondly, Input-to-State Stability (ISS) of the estimation error dynamics is guaranteed in the presence of these perturbations.
Translating Assembly Accuracy Requirements to Cut-Off Frequencies for Component Mode Synthesis
We demonstrate the use of this approach in the scope of component mode synthesis (CMS) methods with the aim to reduce the complexity of component models while guaranteeing accuracy requirements of the assembly model.
Impact Sensitivity Analysis of Cooperative Adaptive Cruise Control Against Resource-Limited Adversaries
We use the size of these sets as a security metric to quantify the potential damage of attacks affecting different signals in a CACC-controlled vehicle and study how two key system parameters change this metric.
Modular Model Reduction of Interconnected Systems: A Top-Down Approach
Models of complex systems often consist of multiple interconnected subsystem/component models that are developed by multi-disciplinary teams of engineers or scientists.
Linear Fault Estimators for Nonlinear Systems: An Ultra-Local Model Design
The proposed method augments the system dynamics with an approximated internal linear model of the combined contribution of known nonlinearities and unknown faults -- leading to an approximated linear model in the augmented state.
Privacy-Preserving Anomaly Detection in Stochastic Dynamical Systems: Synthesis of Optimal Gaussian Mechanisms
We present a framework for designing distorting mechanisms that allow remotely operating anomaly detectors while preserving privacy.
Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption
The idea is to immerse the learning algorithm, a Stochastic Gradient Decent (SGD), into a higher-dimensional system (the so-called target system) and design the dynamics of the target system so that: the trajectories of the original SGD are immersed/embedded in its trajectories, and it learns on encrypted data (here we use random matrix encryption).
Ultra Local Nonlinear Unknown Input Observers for Robust Fault Reconstruction
In this paper, we present a methodology for actuator and sensor fault estimation in nonlinear systems.
Hysteresis-Based RL: Robustifying Reinforcement Learning-based Control Policies via Hybrid Control
Reinforcement learning (RL) is a promising approach for deriving control policies for complex systems.
Gaussian Mechanisms Against Statistical Inference: Synthesis Tools
We formulate the synthesis of distorting mechanisms in terms of semidefinite programs in which we seek to minimize the mutual information (our privacy metric) between private data and the disclosed distorted data given a desired distortion level -- how different actual and distorted data are allowed to be.
