Abstract: Multi parametric quadratic programming gives a full off-line solution to a time varying quadratic programming (QP) problem arising during ...

This paper presents a two degree of freedom algorithm, which achieves a large decrease in both the online computation and data storage requirements with ...

This paper presents a two degree of freedom algorithm, which achieves a large decrease in both the online computation and data storage requirements with ...

We present a methodology to learn explicit Model Predictive Control (eMPC) laws from sample data points with tunable complexity. The learning process is ...

Oct 9, 2024 · We present a method, which allows efficient and safe approximation of model predictive controllers using kernel interpolation. Since the ...

Apr 9, 2020 · A new interpolating control scheme for uncertain linear systems is developed. The interpolation is done between two saturated control laws, ...

Interpolate optimal control law at vertices of complex. Result ... • Explicit MPC with PWA cost has a lifting, but quadratic does not. [Jones et ...

Model predictive control (MPC) is an optimal-control based method to select control inputs by minimizing an objective function. The objective function.

Interpolation methods are one means of tackling the classical performance versus feasibility compromise in model predictive control (MPC).

To use explicit MPC, you need to generate an explicitMPC object from an existing mpc object and then use the mpcmoveExplicit function or the Explicit MPC ...