Investigation into Model-Based Fuzzy Logic Control

Abstract

This thesis investigates the feasibility of a proposed hybrid linear/Fuzzy controller for nonlinear plants. The proposed controller concept is based on the use of multiple linearizations of a nonlinear plant, which describe the dynamics of perturbations about equilibrium points throughout the desired envelope of operation. A bank of linear compensators is developed, each corresponding to a linearized plant about a different equilibrium. The multiple control signals generated by the bank of compensators are then weighted and summed using Fuzzy Logic to produce a composite control perturbation signal, which is used to drive the nonlinear plant. Experiments were conducted to test and refine this control approach. Analysis shows that a linear/Fuzzy compensator based only on a bank of linear compensators is not feasible, largely due to the small regions for which the linearized models were valid and energy considerations within the plant/controller system. The analysis itself, however, suggests an alternate form for a hybrid Linear/Fuzzy approach, based on a bank of Fuzzy compensators smoothed by a linear controller. This concept is developed into the Model-Based Fuzzy Logic Controller (MBFLC). The concept of Fuzzy Logic Model Following Control is also addressed as a second hybrid approach.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1993
Accession Number
ADA274660

Entities

People

  • Michael W. Logan

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Aircrafts
  • Artificial Intelligence
  • Closed Loop Systems
  • Control Systems
  • Control Systems Engineering
  • Control Theory
  • Engineering
  • Equations
  • Families (Human)
  • Fuzzy Logic
  • Fuzzy Sets
  • Linear Systems
  • Logic
  • Mathematical Analysis
  • Nonlinear Dynamics
  • Nonlinear Systems
  • Set Theory

Readers

  • Computer Engineering
  • Operations Research
  • Robotics and Automation.