Stability Analysis of Hypersampled Model Predictive Control

TL;DR

This paper proposes a novel hypersampled model predictive control framework that decouples discretization and sampling times, enhancing stability and performance without increasing computational complexity.

Contribution

It introduces a new stability analysis framework for MPC that separates discretization and sampling times, enabling better control performance.

Findings

Sampling time can be smaller than discretization time for improved stability.

Decoupling sampling and discretization times overcomes traditional performance-complexity trade-offs.

The approach enhances control stability without additional computational burden.

Abstract

This paper introduces a new framework for analyzing the stability of discrete-time model predictive controllers acting on continuous-time systems. The proposed framework introduces the distinction between discretization time (used to generate the optimal control problem) and sampling time (used to implement the controller). The paper not only shows that these two time constants are independent, but also motivates the benefits of selecting a sampling time that is smaller than the discretization time. The resulting approach, hereafter referred to as Hypersampled Model Predictive Control, overcomes the traditional trade-off between performance and computational complexity that arises when selecting the sampling time of traditional discrete-time model predictive controllers.