Estimation and Observer Theory
Real-time state estimation for nonlinear control systems, with an emphasis on convergence guarantees and robustness.
- Nonlinear observers: KKL, PEBO, DREM
- Adaptive observers
- Sensorless control
Research overview · Theory to robotics
Research Overview
My research develops theory and algorithms for estimation, learning, and control of nonlinear dynamical systems, with a strong emphasis on robotics and autonomous systems. The goal is to combine mathematically rigorous guarantees with methods that remain useful on real-world platforms.
Estimation
Learning
Control
Robotics
Research Areas
System Identification and Learning
Data-driven models for dynamical systems that connect system identification with modern learning theory.
- Nonlinear system identification
- Koopman operator learning
- Real-time parameter estimation
Nonlinear Control
Control design for uncertain nonlinear systems, drawing on structural and energy-based viewpoints.
- Adaptive control
- Orbital stabilization
- Passivity-based control
- Contraction analysis
Robotics and Autonomous Systems
Estimation, learning, and control methods for robotic systems operating in complex and uncertain environments.
- Continuum robots
- Navigation and localization
- Autonomous systems
Featured Tutorials & Slides
Parameter estimation-based observer (PEBO)
A tutorial introduction to observer design through parameter estimation.
View PDFHigh-performance sensorless estimation for IPMSMs
Selected slides on observer design and nonlinear estimation.
View PDFKoopman learning
Learning representations and models for nonlinear dynamical systems.
View PDFOrbital stabilization
Nonlinear methods for stabilizing periodic motions and target orbits.
View PDFModeling, stiffening and control of continuum robots
Selected work connecting nonlinear systems theory and robotics.
View PDFExplore related work, people, and opportunities.