Ensuring Stability in Networked Systems with Nonlinear MPC for Continuous Time Systems

TL;DR

This paper establishes a stability theorem for nonlinear model predictive control in continuous time networked systems, demonstrating stability under variable control horizons without the need for terminal constraints or costs.

Contribution

It introduces a stability result for nonlinear MPC with varying control horizons in continuous time, eliminating the need for terminal constraints or costs.

Findings

Stability can be ensured with varying control horizons under the same conditions as fixed horizons.

The approach applies to networked systems with delays and packet dropouts.

No stabilizing terminal constraints or costs are required.

Abstract

For networked systems, the control law is typically subject to network flaws such as delays and packet dropouts. Hence, the time in between updates of the control law varies unexpectedly. Here, we present a stability theorem for nonlinear model predictive control with varying control horizon in a continuous time setting without stabilizing terminal constraints or costs. It turns out that stability can be concluded under the same conditions as for a (short) fixed control horizon.