Inexact methods for model predictive control (MPC), such as real-time iterative schemes or
time-distributed optimization, alleviate the computational burden of exact MPC by providing …

This article introduces an approach for reducing the computational cost of implementing
linear quadratic model predictive control (MPC) for set-point tracking subject to pointwise-in …

In distributed model predictive control (MPC), the control input at each sampling time is
computed by solving a large-scale optimal control problem (OCP) over a finite horizon using …

Limited computation resources forces early (sub-optimal) termination of the solvers used for
model predictive controllers (MPCs). This can compromise the feasibility and stability …

Suboptimal methods in optimal control arise due to a limited computational budget,
unknown system dynamics, or a short prediction window among other reasons. Although …

We show that quadratic constraints admit a semi-algebraic interpretation of dynamic
systems. This allows us to improve the analysis of polynomial systems under nonlinear …

In the setting of discrete-time linear systems with unmeasured set-bounded disturbances,
this paper proposes a control scheme that combines a disturbance free input-constrained …

This paper introduces a supervisory scheme, called the iteration governor (IG), that
augments a suboptimal input-constrained MPC policy by performing online selection of the …

The application of distributed model predictive controllers (DMPC) for multi-agent systems
(MASs) necessitates communication between agents, yet the consequence of …

Many practical applications of optimal control are subject to real-time computational
constraints. When applying model predictive control (MPC) in these settings, respecting …