For constrained linear systems with bounded disturbances and parametric uncertainty, we propose a robust adaptive model predictive control strategy online parameter estimation. Constraints enforcing persistently exciting closed loop actions are introduced for set-membership identification scheme. The algorithm requires the solution of convex program, satisfies constraints robustly, ensures recu...

This paper studies the problem of distributionally robust model predictive control (MPC) using total variation distance ambiguity sets. For a discrete-time linear system with additive disturbances, we provide conditional value-at-risk reformulation MPC optimization that is in expected cost and chance constraints. The constraint over-approximated as simpler, tightened reduces computational burde...

Koopman operators are of infinite dimension and capture the characteristics nonlinear dynamics in a lifted global linear manner. The finite data-driven approximation results class predictors, useful for formulating model predictive control (MPC) dynamical systems with reduced computational complexity. However, robustness closed-loop MPC under modeling errors possible exogenous disturbances is s...

the control of car following is essential due to its safety and its operational efficiency. for this purpose, this paper builds a model of car following behavior based on armax structure from a real traffic dataset and design a model predictive control (mpc) system. based on the relative distance and relative acceleration of each instant, the mpc predicts the future behavior of the leader vehic...