# Event-Triggered Intermittent Sampling for Nonlinear Model Predictive Control

## TL;DR

This paper introduces a new event-triggered sampling method for nonlinear model predictive control that reduces sensing costs and is more practical for digital implementations by avoiding continuous monitoring and conservativeness.

## Contribution

It presents a novel aperiodic MPC formulation with event-triggered conditions based on local set entry, eliminating the need for Lyapunov-based costs and continuous checking.

## Key findings

- Reduces sensing and computational costs in nonlinear MPC.
- Avoids conservativeness associated with Lipschitz parameters.
- Validated through numerical simulations demonstrating effectiveness.

## Abstract

In this paper, we propose a new aperiodic formulation of model predictive control for nonlinear continuous-time systems. Unlike earlier approaches, we provide event-triggered conditions without using the optimal cost as a Lyapunov function candidate. Instead, we evaluate the time interval when the optimal state trajectory enters a local set around the origin. The obtained event-triggered strategy is more suitable for practical applications than the earlier approaches in two directions. First, it does not include parameters (e.g., Lipschitz constant parameters of stage and terminal costs) which may be a potential source of conservativeness for the event-triggered conditions. Second, the event-triggered conditions are necessary to be checked only at certain sampling time instants, instead of continuously. This leads to the alleviation of the sensing cost and becomes more suitable for practical implementations under a digital platform. The proposed event-triggered scheme is also validated through numerical simulations.