Quantitative Synthesis of Uncertain Multiple Input-Output Feedback System,

Abstract

There is given an n input, n output plant with a specified range of parameter uncertainty and specified tolerances on the n to the 2nd power system response to command functions and the n to the 2nd power response to disturbance functions. It is shown how Schauder's fixed point theorem may be used to generate a variety of synthesis techniques, for a large class of such plants. The design guarantees the specifications are satisfied over the range of parameter uncertainty. An attractive property is that design execution is that of successive single-loop designs, with no interaction between them and no iteration necessary. Stability over the range of parameter uncertainty is automatically included. By additional use of Schauder's theorem, these same synthesis techniques can be rigorously used for quantitative design in the same sense as above, for n x n uncertain nonlinear plants, even nonlinear time-varying plants, in response to a finite number of inputs. Latter approach sufficient. However, a scientific theory of feedback should certainly include quantitative design techniques. In this paper, it is shown how Schauder's fixed point theorem can be used to generate a variety of precise quantitative mio synthesis techniques suitable for various problem classes.

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Document Details

Document Type
Technical Report
Publication Date
Aug 31, 1979
Accession Number
ADA087014

Entities

People

  • Isaac M. Horowitz

Organizations

  • Weizmann Institute of Science

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Banach Space
  • Bandwidth
  • Closed Loop Systems
  • Control Systems
  • Control Systems Engineering
  • Feedback
  • Frequency
  • Functional Analysis
  • Guarantees
  • New York
  • Point Theorem
  • Rational Functions
  • Scientific Theories
  • Specifications
  • Time Domain
  • Transfer Functions
  • Uncertainty

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Control Systems Engineering.