PID-Based Event-Triggered MPC for Constrained Nonlinear Cyber-Physical Systems: Theory and Application

Model predictive control (MPC), which obtains the control signal via solving an optimal control problem online, has been widely applied in various cyber-physical systems (CPSs) due to its ability to handle the physical constraints on system variables explicitly. To further reduce the communication a...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2024-10, Vol.71 (10), p.13103-13112
Main Authors: He, Ning, Li, Yuxiang, Li, Huiping, He, Dangtong, Cheng, Fuan
Format: Article
Language:English
Subjects:
Algorithms
and differential (PID)-based strategy
Behavioral sciences
Constraints
Control systems
Cyber-physical systems
Cyber-physical systems (CPSs)
Errors
event triggering (ET) strategy
integral
model predictive control (MPC)
Nonlinear control
Nonlinear dynamical systems
Nonlinear systems
Optimal control
Predictive control
proportion
Robot control
robot platform
Robots
Upper bound
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Model predictive control (MPC), which obtains the control signal via solving an optimal control problem online, has been widely applied in various cyber-physical systems (CPSs) due to its ability to handle the physical constraints on system variables explicitly. To further reduce the communication and computational resource consumption while ensuring the control performance, a novel event-triggered MPC (ET-MPC) framework is proposed for perturbed nonlinear CPSs with input and state constraints. Specifically, rather than utilizing the instantaneous state error to determine the next triggering instant, which may result in residual error and state mutation for the considered system, the developed strategy adopts the proportion, integral, and differential (PID) of the state error within a time horizon simultaneously to ensure the performance of the CPS under the event-based sampling framework. Moreover, the PID-based ET-MPC algorithm is developed, and the theoretical properties, including feasibility and stability, are strictly proved. Finally, the resource saving property, as well as the control performance of the proposed ET-MPC strategy are extensively verified via MATLAB simulation and a real robot platform powered by the robot operating system, respectively.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2024.3357846