6 papers
Asymmetric Nash Seeking via Best Response Maps: Global Linear Convergence and Robustness to Inexact Reaction Models
Mahdis Rabbani, Navid Mojahed, Shima Nazari
Nash equilibria provide a principled framework for modeling interactions in multi-agent decision-making and control. However, many equilibrium-seeking methods implicitly assume tha…
Koopman Lifted Finite Memory Identification via Truncated Grunwald Letnikov Kernels
Navid Mojahed, Mahdis Rabbani, Shima Nazari
We propose a data-driven linear modeling framework for controlled nonlinear hereditary systems that combines Koopman lifting with a truncated Grunwald-Letnikov memory term. The key…
Optimal Modified Feedback Strategies in LQ Games under Control Imperfections
Mahdis Rabbani, Navid Mojahed, Shima Nazari
Game-theoretic approaches and Nash equilibrium have been widely applied across various engineering domains. However, practical challenges such as disturbances, delays, and actuator…
A Data Driven Structural Decomposition of Dynamic Games via Best Response Maps
Mahdis Rabbani, Navid Mojahed, Shima Nazari
Dynamic games are powerful tools to model multi-agent decision-making, yet computing Nash (generalized Nash) equilibria remains a central challenge in such settings. Complexity ari…
Predictive Compensation in Finite-Horizon LQ Games under Gauss-Markov Deviations
Navid Mojahed, Mahdis Rabbani, Shima Nazari
This paper develops a predictive compensation framework for finite-horizon, discrete-time linear quadratic dynamic games subject to Gauss-Markov execution deviations from feedback…
Multi-Step Deep Koopman Network (MDK-Net) for Vehicle Control in Frenet Frame
Mohammad Abtahi, Mahdis Rabbani, Armin Abdolmohammadi +1
The highly nonlinear dynamics of vehicles present a major challenge for the practical implementation of optimal and Model Predictive Control (MPC) approaches in path planning and f…