Quantitative Robust Control Engineering: Theory and Applications

Abstract

This paper presents a summary of the main concepts and references of the Quantitative Feedback Theory (QFT). It is a frequency domain engineering method to design robust controllers. It explicitly emphasizes the use of feedback to simultaneously reduce the effects of model plant uncertainty and to satisfy performance specifications. QFT highlights the trade-off (quantification) among the simplicity of the controller structure, the minimization of the cost of feedback, the existing model uncertainty and the achievement of the desired performance specifications at every frequency of interest. The technique has been successfully applied to control a wide variety of physical systems. After a brief introduction about the essential aspects of the QFT design methodology, including a wide set of QFT references, this paper presents a new method to extend the classical diagonal QFT controller design method for MIMO plants with model uncertainty to a fully populated matrix controller design method. The paper simultaneously studies three cases: the reference tracking, the external disturbance rejection at plant input and the external disturbance rejection at plant output. The work ends showing several real-world examples where the controllers have been designed using QFT techniques: an industrial SCARA robot manipulator, a wastewater treatment plant, a variable speed wind turbine of 1.65 MW and an industrial furnace of 1 MW.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2006
Accession Number
ADA470878

Entities

People

  • Mario Garcia-sanz

Organizations

  • Public University of Navarre

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Bandwidth
  • Closed Loop Systems
  • Control Systems
  • Control Systems Engineering
  • Engineering
  • Feedback
  • Flight Control Systems
  • Frequency Domain
  • Linear Programming
  • Mathematical Models
  • Multiple Input Multiple Output
  • Nitrogen Compounds
  • Standards
  • Systems Engineering
  • Systems Science
  • Wind Turbines

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control