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Approximate Feedback Nash Equilibria in Constrained Differential Games via MPC
US & West ·
A Model Predictive Control approach approximates infinite-horizon feedback Nash equilibrium trajectories by repeatedly solving finite-horizon open-loop games. It targets multi-agent control settings, including physical human-machine interaction, where agent responses and state/input constraints must be respected.
Why it matters
It offers a computationally tractable way to derive time-consistent control strategies in systems with multiple interacting agents and explicit constraints.
Original headline
Approximate Feedback Nash Equilibria in Constrained Differential Games via Model Predictive Control with Upper Bound Guarantees
arXiv:2610.29559v1 Announce Type: new Abstract: Model predictive control is a high-performance control method, but it incurs comparatively high online computational costs across many control tasks and is challenging to realize in low-level hardware settings.
arXiv:2610.29377v1 Announce Type: cross Abstract: Learning-based path planners based on diffusion and flow matching can generate diverse trajectories from demonstrations but typically lack mechanisms for constraint satisfaction and collision avoidance during deployment.
arXiv:2604.04455v3 Announce Type: replace Abstract: Nonlinear underactuated systems such as two-wheeled inverted pendulums (TWIPs) exhibit a limited region of attraction (RoA), which defines the set of initial conditions from which the closed-loop system converges to the equilibrium.
