This book focuses on the distributed control and estimation of large-scale networked distributed systems and the approach of distributed model predictive and moving horizon estimation. Both principles and engineering practice have been addressed, with more weight placed on engineering practice. This is achieved by providing an in-depth study on several major topics such as the state estimation and control design for the networked system with considering time-delay, data-drop, etc., Distributed MPC design for improving the performance of the overall networked system, which includes several classic strategies for different scenarios, details of the application of the distributed model predictive control to smart grid system and distributed water network. The comprehensive and systematic treatment of theoretical and practical issues in distributed MPC for networked systems is one of the major features of the book, which is particularly suited for readers who are interested to learn practical solutions in distributed estimation and optimization of distributed networked systems. The book benefits researchers, engineers, and graduate students in the fields of chemical engineering, control theory and engineering, electrical and electronic engineering, chemical engineering, and computer engineering, etc.
Author(s): Shaoyuan Li, Yi Zheng, Binqiang Xue
Series: Advanced and Intelligent Manufacturing in China
Publisher: Springer-CIP
Year: 2023
Language: English
Pages: 272
City: Shanghai
Contents
1 Status of Research on Networked Distributed Systems
1.1 Background
1.2 Status of Research on Predictive Control for Networked Distributed Systems
1.2.1 Current Status of Research on Networked Moving Horizon Estimation
1.2.2 Status of Research and Classification of Distributed Predictive Control
1.3 Main Contents of the Book
References
2 Moving Horizon State Estimation for Networked Systems with Random Packet Loss
2.1 Overview
2.2 Moving Horizon State Estimation for Networked Systems with Feedback-Channel Packet Loss
2.2.1 Description of the Problem
2.2.2 Networked Moving Horizon State Estimator
2.2.3 Performance Analysis of the Estimator
2.2.4 Numerical Simulation
2.3 Moving Horizon State Estimation for Networked Systems with Two-Channel Packet Loss
2.3.1 Description of the Problem
2.3.2 Networked Moving Horizon State Estimator
2.3.3 Performance Analysis of the Estimator
2.3.4 Numerical Simulations
2.4 Summary of This Chapter
References
3 Design of Predictive Controller for Networked Systems
3.1 Overview
3.2 Predictive Control for Networked Control Systems with Bounded Packet Loss
3.2.1 Modelling of Networked Control Systems
3.2.2 Networked Predictive Controller Based on Terminal Convex Set Constraints
3.2.3 Feasibility and Stability Analysis of Networked Predictive Controllers
3.2.4 Numerical Simulation
3.3 Robust Predictive Control of Networked Control Systems With Control Input Quantization
3.3.1 Modelling of Networked Control Systems
3.3.2 Stability Analysis and Robust Predictive Controller Design
3.3.3 Numerical Simulations
3.4 Summary of This Chapter
References
4 Moving Horizon Scheduling for Networked Systems with Communication Constraints
4.1 Overview
4.2 Networked Moving Horizon Scheduling
4.2.1 Description of the Problem
4.2.2 Moving Horizon Scheduling Strategy
4.3 Moving Horizon State Estimation for Networked Control Systems
4.4 Performance Analysis of Networked Moving Horizon Estimators
4.5 Numerical Simulation and Physical Experiments
4.5.1 Numerical Simulation
4.5.2 Experiments on Two-Tank Liquid-Level Systems
4.6 Summary of This Chapter
References
5 Distributed Predictive Control for Local Performance Index
5.1 Overview
5.2 Nash Optimal Based Distributed Predictive Control
5.2.1 Distributed Predictive Controller Design
5.2.2 Performance Analysis
5.2.3 Performance Analysis of the One-Step Prediction Optimization Strategy for Local Communication Failures
5.2.4 Simulation Example
5.3 Constrained Distributed Predictive Control with Guaranteed Stability
5.3.1 Description of the Problem
5.3.2 Distributed Predictive Control Design
5.3.3 Performance Analysis
5.3.4 Simulation Example
5.4 Summary of This Chapter
References
6 Cooperative Distributed Predictive Control System
6.1 Overview
6.2 Non-iterative Cooperative Distributed Predictive Control
6.2.1 State-, Input-Coupled Distributed Systems
6.2.2 Local Predictive Controller Design
6.2.3 Performance Analysis
6.2.4 Simulation Example
6.3 Constrained Coordinated Distributed Predictive Control with Guaranteed Stability [3]
6.3.1 Description of Distributed Systems
6.3.2 Local Predictive Controller Design
6.3.3 Performance Analysis
6.3.4 Simulation Example
6.4 Summary of This Chapter
References
7 Distributed Predictive Control Under Communication Constraints
7.1 Overview
7.2 Distributed Predictive Control Based on Neighborhood Optimization
7.2.1 State-, Input-Coupled Distributed Systems
7.2.2 Local Predictive Controller Design
7.2.3 Performance Analysis
7.2.4 Numerical Results
7.3 Stabilized Neighborhood Optimization Based Distributed Model Predictive Control
7.3.1 Problem Description
7.3.2 DMPC Design
7.3.3 Stability and Convergence
7.3.4 Simulation
7.4 Summary of This Chapter
References
8 Application of Distributed Model Predictive Control in Accelerated Cooling Process
8.1 Overview
8.2 Accelerated Cooling Process
8.2.1 Accelerated Cooling Process and Plant Instrumentation
8.2.2 Accelerated Cooling Process Simulation Platform
8.2.3 Process Control Requirements
8.3 Heat Balance Equation for the Unit
8.4 Distributed Predictive Control Based on Optimal Objective Recalculation
8.4.1 Subsystem Optimization Objective Recalculation
8.4.2 Subsystem State Space Model
8.4.3 Extending the Kalman Global Observer
8.4.4 Local Predictive Controller
8.4.5 Local State Prognosticator
8.4.6 Local Controller Iterative Solution Algorithm
8.5 Simulation Platform Algorithm Validation
8.6 Summary of This Chapter
References
Index
