Self-triggered Model Predictive Control for Nonlinear Input-Affine Dynamical Systems via Adaptive Control Samples Selection

TL;DR

This paper introduces a self-triggered model predictive control approach for nonlinear input-affine systems that reduces communication load by adaptively selecting control samples while ensuring system stability.

Contribution

It presents a novel self-triggered MPC framework with adaptive control sample selection for nonlinear systems, including stability guarantees and practical simulation validation.

Findings

Reduced communication load through adaptive sampling

Guaranteed stability with sample-and-hold implementation

Validated effectiveness via simulation examples

Abstract

In this paper, we propose a self-triggered formulation of Model Predictive Control for continuous-time nonlinear input-affine networked control systems. Our control method specifies not only when to execute control tasks but also provides a way to discretize the optimal control trajectory into several control samples, so that the reduction of communication load will be obtained. Stability analysis under the sample-and-hold implementation is also given, which guarantees that the state converges to a terminal region where the system can be stabilized by a local state feedback controller. Some simulation examples validate our proposed framework.