4 citations · 4 across the 7 of their papers we have counts for
7 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…
Structured Koopman Lifted Finite Memory Identification via Truncated Grunwald Letnikov Kernels
Navid Mojahed, Mahdis Rabbani, Shima Nazari
Nonlinear hereditary systems combine nonlinear state dependence with memory. Koopman lifting provides linear predictors for nonlinear dynamics, but standard formulations are Markov…
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…
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…
An Automatic Tuning MPC with Application to Ecological Cruise Control
Mohammad Abtahi, Mahdis Rabbani, Shima Nazari
Model predictive control (MPC) is a powerful tool for planning and controlling dynamical systems due to its capacity for handling constraints and taking advantage of preview inform…