Book Chapter

Networked Distributed Predictive Control with Information Structure Constraints

Li, Shaoyuan, Zheng, Yi

2015

Overview

Designing a distributed model predictive control (DMPC) which could significantly improve the global performance of the closed‐loop system with limited information structure constraints is valuable. The optimization objective of each subsystem‐based MPC considers not only the performance of the corresponding local subsystem but also those it has a direct impact on. The chapter describes the noniterative networked DPC and gives its closed‐form solution. The closed‐loop system will achieve “Nash optimality” if the iterative algorithm is employed in the LCO‐DMPC and the closed‐loop system could obtain “Pareto optimality” if the iterative algorithm is employed in the global cost optimization‐based DMPC. The chapter analyzes the convergent condition of the proposed networked predictive control algorithm and the nominal stability for distributed control systems without inequality constraints. An illustrative example of the fuel feed flow control for the walking beam reheating furnace is presented to verify the efficiency of the proposed networked MPC algorithms.

• Publisher: John Wiley & Sons, Singapore Pte. Ltd
• Subject: closed‐loop system / distributed control system / distributed model predictive control / DMPC iterative algorithm / Nash optimality / networked MPC algorithm / Pareto optimality / performance index / structure constraint
• ISBN: 9781118921562, 1118921569