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- Stability Theory and Its Applications to Neural Networks
We investigate rigorous stability and robustness properties of neural network–based dynamical systems.
Our research develops Lyapunov-based analysis, delay-dependent conditions, and prescribed performance guarantees for neural ODEs and recurrent architectures.
These theoretical results enable the systematic design of stable and reliable learning-enabled estimators and controllers.
2. Control and Estimation of Nonlinear Systems
2. Control and Estimation of Nonlinear Systems
We investigate rigorous stability and robustness properties of neural network–based dynamical systems.
Our research develops Lyapunov-based analysis, delay-dependent conditions, and prescribed performance guarantees for neural ODEs and recurrent architectures.
These theoretical results enable the systematic design of stable and reliable learning-enabled estimators and controllers.
3. Advanced Control of Robotic Systems
3. Advanced Control of Robotic Systems
We develop advanced control and estimation frameworks for complex robotic systems operating under nonlinearities, uncertainties, and input constraints.
Our approach integrates model-based control theory with data-driven methods to enhance stability, robustness, and real-time performance.
Applications include autonomous robots and intelligent robotic platforms in dynamic environments.
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4. Analysis and Control of Vehicle Dynamics
4. Analysis and Control of Vehicle Dynamics
We study the modeling, analysis, and control of vehicle dynamics with a focus on stability, robustness, and performance enhancement.
Our research addresses nonlinear tire dynamics, uncertainties, and time-delay effects in autonomous driving systems.
Advanced control methodologies are designed to improve safety, maneuverability, and tracking performance.
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