LECTURE SERIES IN DIFFERENTIAL EQUATIONS, SESSION I. CONTROL THEORY

Abstract

The Scientific report summarizes the three lectures presented at the First Session of the Lecture Series in Differential Equations. Professor Dantzig illustrated how mathematical programming, and in particular a generalized linear program, can be applied to a linear control process. The 'problem' takes the form of minimizing the 'cost' of moving an 'object' from one convex domain to another by proper choice of a control vector and boundary conditions. Professor Lefschetz dealt with the Lurie problem on nonlinear controls, i.e., the determination of necessary and sufficient conditions such that all solutions of a set of general nonlinear control differential equations are absolutely stable in the large whatever the choice of the admissible (function) characteristic of the control. Professor Markus discussed the 'bang-bang' theory of control as a physical concept and as a collection of precise mathematical theorems. The bang- bang principal states that any response of a controlled system which can be achieved by an arbitrary controller restricted to the extreme values of the control domains. Theorems presented relate both to linear and nonlinear control processes.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1965
Accession Number
AD0828318

Entities

People

  • George Bernard Dantzig
  • Lawrence Markus
  • Solomon Lefschetz

Organizations

  • Georgetown University

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Air Force
  • Algebra
  • Boundaries
  • Control Systems
  • Control Theory
  • Convex Sets
  • Differential Equations
  • Equations
  • Inequalities
  • Linear Programming
  • Mathematical Programming
  • Operations Research
  • Polynomials
  • Scientific Research
  • Theorems
  • Transfer Functions
  • United States

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Calculus or Mathematical Analysis
  • Linear Algebra