Stochastic Distribution Control System Design

A Convex Optimization Approach

  • Lei Guo
  • Hong Wang

Part of the Advances in Industrial Control book series (AIC)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Developments in Stochastic Distribution Control Systems

  3. Structural Controller Design for Stochastic Distribution Control Systems

  4. Two-step Intelligent Optimization Modeling and Control for Stochastic Distribution Control Systems

  5. Statistical Tracking Control – Driven by Output Statistical Information Set

  6. Fault Detection and Diagnosis for Stochastic Distribution Control Systems

  7. Conclusions

    1. Front Matter
      Pages 171-171
  8. Back Matter
    Pages 179-195

About this book


Stochastic distribution control (SDC) systems are widely seen in practical industrial processes, the aim of the controller design being generation of output probability density functions for non-Gaussian systems. Examples of SDC processes are: particle-size-distribution control in chemical engineering, flame-distribution control in energy generation and combustion engines, steel and film production, papermaking and general quality data distribution control for various industries. SDC is different from well-developed forms of stochastic control like minimum-variance and linear-quadratic-Gaussian control, in which the aim is limited to the design of controllers for the output mean and variances.

An important recent development in SDC-related problems is the establishment of intelligent SDC models and the intensive use of linear-matrix-inequality-based (LMI-based) convex optimization methods. Within this theoretical framework, control parameter determination can be designed and stability and robustness of closed-loop systems can be analyzed. Stochastic Distribution Control System Design describes the new framework of SDC system design and provides a comprehensive description of the modelling of controller design tools and their real-time implementation. The book starts with a review of current research on SDC and moves on to some basic techniques for modelling and controller design of SDC systems. This is followed by a description of controller design for fixed-control-structure SDC systems, PDF control for general input- and output-represented systems, filtering designs, and fault detection and diagnosis (FDD) for SDC systems. Many new LMI techniques being developed for SDC systems are shown to have independent theoretical significance for robust control and FDD problems.

This monograph will be of interest to academic researchers in statistical, robust and process control, and FDD, process and quality control engineers working in industry and as a reference for graduate control students.



Tracking algorithm chemical engineering fault detection fuzzy industrial process model modeling neural network optimization robust control stability time delay

Authors and affiliations

  • Lei Guo
    • 1
  • Hong Wang
    • 2
    • 3
  1. 1.Institute of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
  2. 2.Northeastern UniversityShenyangChina
  3. 3.School of Electrical and Electronic EngineeringUniversity of ManchesterManchesterUK

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag London 2010
  • Publisher Name Springer, London
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-1-84996-029-8
  • Online ISBN 978-1-84996-030-4
  • Series Print ISSN 1430-9491
  • Series Online ISSN 2193-1577
  • Buy this book on publisher's site