activity
20162026
most citedDynamic Mode Decomposition of Control-Affine Nonlinear Systems using Discrete Control Liouville Operators

2 citations · 8 across the 32 of their papers we have counts for

collaborators
Showing eess.SYShow all

36 papers · 1 filter

eess.SY2026

Switching Observers for Linear Systems: Beyond Individual Observability

Masoud S. Sakha, Rushikesh Kamalapurkar

This paper develops an LMI-based switching observer for linear time-invariant systems with two output channels, where only one output is available at any given time. We assume that…

eess.SY2025

On the embedding transformation for optimal control of multi-mode switched systems

Masoud S. Sakha, Rushikesh Kamalapurkar

This paper develops an embedding-based approach to solve switched optimal control problems (SOCPs) with an arbitrary number of subsystems. Initially, the discrete switching signal…

eess.SY2025

Lyapunov-Based Physics-Informed Deep Neural Networks with Skew Symmetry Considerations

Rebecca G. Hart, Wanjiku A. Makumi, Rushikesh Kamalapurkar +1

Deep neural networks (DNNs) are powerful black-box function approximators which have been shown to yield improved performance compared to traditional neural network (NN) architectu…

eess.SY2025

Safe Output-Feedback Adaptive Optimal Control of Affine Nonlinear Systems

Tochukwu E. Ogri, Muzaffar Qureshi, Zachary I. Bell +2

In this paper, we develop a safe control synthesis method that integrates state estimation and parameter estimation within an adaptive optimal control (AOC) and control barrier fun…

eess.SY2025

Improved Dwell-times for Switched Nonlinear Systems using Memory Regression Extension

Muzaffar Qureshi, Tochukwu Elijah Ogri, Humberto Ramos +3

This paper presents a switched systems approach for extending the dwell-time of an autonomous agent during GPS-denied operation by leveraging memory regressor extension (MRE) techn…

eess.SY2025

A Taylor Series Approach to Correction of Input Errors in Gaussian Process Regression

Muzaffar Qureshi, Tochukwu Elijah Ogri, Zachary I. Bell +2

Gaussian Processes (GPs) are widely recognized as powerful non-parametric models for regression and classification. Traditional GP frameworks predominantly operate under the assump…