Control of Uncertain Systems with a Set-Membership Description of the Uncertainty

Abstract

The problem of optimal feedback control of uncertain discrete-time dynamic systems is considered, where the uncertain quantities do not have a stochastic description but instead they are known to belong to given sets. The problem is converted to a sequential minimax problem and dynamic programming is suggested as a general method for its solution. The notion of a sufficiently informative function, which parallels the notion of a sufficient statistic of stochastic optimal control, is introduced, and the possible decomposition of the optimal controller into an estimator and an actuator is demonstrated. Some special cases involving a linear system are further examined. A problem involving a convex cost functional and perfect state information for the controller is considered in detail. Particular attention is given to a special case, the problem of reachability of a target tube, and an ellipsoidal approximation algorithm is obtained which leads to linear control laws. State estimation problems are also examined, and some algorithms are derived which offer distinct advantages over existing estimation schemes. These algorithms are subsequently used in the solution of some reachability problems with imperfect state information for the controller.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1971
Accession Number
AD0728720

Entities

People

  • Dimitri P. Bertsekas

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Closed Loop Systems
  • Computational Science
  • Computer Programming
  • Control Systems
  • Dynamic Programming
  • Electrical Engineering
  • Engineering
  • Estimators
  • Feedback
  • Game Theory
  • Information Science
  • Linear Systems
  • Mathematical Models
  • Optimization
  • Riccati Equation
  • Servomechanisms

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.