| Document Type
|
:
|
BL
|
| Record Number
|
:
|
889305
|
| Main Entry
|
:
|
C., Anthony Tri Tran
|
| Title & Author
|
:
|
A quadratic constraint approach to model predictive control of interconnected systems /\ Anthony Tri Tran C., Quang Ha.
|
| Publication Statement
|
:
|
Singapore :: Springer,, [2018]
|
|
|
:
|
, ©2018
|
| Series Statement
|
:
|
Studies in systems, decision and control ;; 148
|
| Page. NO
|
:
|
1 online resource
|
| ISBN
|
:
|
9789811084096
|
|
|
:
|
: 9811084092
|
|
|
:
|
9789811084072
|
|
|
:
|
9811084076
|
| Bibliographies/Indexes
|
:
|
Includes bibliographical references.
|
| Contents
|
:
|
Intro; Preface; Contents; List of Figures; 1 Introduction; 1.1 General; 1.2 Quadratic Constraintâ#x80;#x94;a Time-Domain Perspective; 1.2.1 Positive Supply Power; 1.2.2 Energy-Dissipative Motion; 1.2.3 Predictive PID Based on Energy-Dissipativity; 1.3 General Dissipativity Constraint; 1.3.1 System Model; 1.3.2 Supply Rates with Compound Vectors; 1.3.3 Stability; 1.3.4 Passivity and Small-Gain Theorems; 1.3.5 Nucleus Contributions; 2 Quadratic Constraint for Decentralised Model Predictive Control; 2.1 Control and System Models; 2.2 Asymptotic Attractivity Condition; 2.2.1 Quadratic Constraint.
|
|
|
:
|
2.2.2 Attractivity Constraint and Its Qualification2.2.3 Attractivity Condition for Unconstrained Systems; 2.3 Decentralised Model Predictive Control and Quadratic Constraint; 2.3.1 Decentralised Model Predictive Control; 2.3.2 Centralised Moving Horizon State Estimation; 2.3.3 Attractivity Condition for Control-Constrained Systems; 2.4 Decentralised MPC with Quadratic Constraint Algorithm; 2.4.1 Procedure; 2.4.2 Determination of the QDC Coefficient Matrices; 2.5 Numerical Simulation; 2.5.1 Illustrative Example 1; 2.5.2 Illustrative Example 2; 2.5.3 Illustrative Example 3.
|
|
|
:
|
2.6 Concluding Remarks3 Quadratic Constraint for Parallel Splitting Systems; 3.1 System and Control Model; 3.1.1 Serial Connection; 3.1.2 Parallelised Connection; 3.1.3 Global System; 3.2 Parallel Splitting System with a Matrix Annihilation; 3.2.1 Asymptotically Surely Positive Realness Constraint and Attractability Condition; 3.2.2 Decentralised MPC for Parallel Splitting Systems; 3.3 Parallelised Masking Dissipativity Criterion; 3.3.1 Subsystem Control Model; 3.3.2 Unit Control Model; 3.3.3 Global System Control Model; 3.3.4 Subsystem Stand-Alone Control Model.
|
|
|
:
|
3.3.5 Dissipative and Attractive Conditions3.4 Numerical Examples; 3.4.1 Decentralised MPC Without Control Constraint; 3.4.2 Decentralised MPC with Control Constraint; 3.4.3 Decentralised MPC with Control Constraint and ASPRC; 3.5 Concluding Remarks; 4 Quadratic Constraint for Semi-automatic Control; 4.1 Semi-automatic Control; 4.2 Stabilising Agent Operation; 4.3 Constructive Procedure for Stabilising Agents; 4.3.1 Stabilising Agent Procedure; 4.3.2 Graphical Presentation; 4.4 Stabilising Agent with Output Tracking; 4.4.1 Steady-State-Independent Quadratic Constraint.
|
|
|
:
|
4.4.2 Convergence Condition with Output Tracking4.4.3 Stabilising Agent with Output-Tracking Algorithm; 4.4.4 Control Algorithm; 4.5 Illustrative Examples; 4.5.1 Illustrative Example 1â#x80;#x94;Power Systems; 4.5.2 Illustrative Example 2â#x80;#x94;Network Process System; 4.6 Concluding Remarks; 5 Quadratic Constraint with Data Losses; 5.1 Introduction; 5.2 System and Networked Control Models; 5.2.1 System Model; 5.2.2 Deterministic Data-Lost Process; 5.3 Dissipative Condition for Networked Control Systems; 5.4 Stability Condition for Networked Control Systems.
|
| Abstract
|
:
|
This book focuses on the stabilization and model predictive control of interconnected systems with mixed connection configurations. It introduces the concept of dissipation-based quadratic constraint for developing attractivity assurance methods for interconnected systems. In order to develop these methods, distributed and decentralized architectures are employed, whereby the communication between subsystems is fully connected, partially connected, or completely disconnected. Given that the control inputs are entirely or partially decoupled between subsystems and no additional constraints are imposed on the interactive variables beyond the coupling constraint itself, the proposed approaches can be used with various types of systems and applications. Further, the book describes how the effects of coupling delays and data losses in device networks are resolved. From a practical perspective, the innovations presented are of benefit in applications in a broad range of fields, including the process and manufacturing industries, networked robotics, and network-centric systems such as chemical process systems, power systems, telecommunication networks, transportation networks, and, no less importantly, supply chain automation.
|
| Subject
|
:
|
Large scale systems.
|
| Subject
|
:
|
Predictive control.
|
| Subject
|
:
|
Artificial intelligence.
|
| Subject
|
:
|
Automatic control engineering.
|
| Subject
|
:
|
Communications engineering-- telecommunications.
|
| Subject
|
:
|
Large scale systems.
|
| Subject
|
:
|
Predictive control.
|
| Subject
|
:
|
SCIENCE-- System Theory.
|
| Subject
|
:
|
TECHNOLOGY ENGINEERING-- Operations Research.
|
| Dewey Classification
|
:
|
003.5
|
| LC Classification
|
:
|
TJ217.6
|
| Added Entry
|
:
|
Ha, Quang
|