Optimization-Based Robust Nonlinear Control

Abstract

New control algorithms were developed for robust stabilization of nonlinear dynamical systems. Novel, linear matrix inequality-based synthesis algorithms were developed for the anti-windup problem. Results were extended so that they are applicable to all stabilizable linear systems with input saturation. New insights into the closed-loop behavior of model predictive control were discovered. It was shown that certain model predictive control algorithms induce stability without any robustness. Then it was shown how these algorithms can be modified to guarantee robustness. Formulas for horizon length to guarantee robust stabilization were given, and in the process it was shown that many of the standard assumptions in model predictive control could be relaxed. Extremum seeking control algorithms were advanced, and developed for systems with constraints and with nonsmooth response maps. Finally, some initial progress was made on the use of logical elements in nonlinear control algorithms and the understanding of hybrid control systems in general.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2006
Accession Number
ADA452020

Entities

People

  • Andrew R. Teel

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Adaptive Control Systems
  • Closed Loop Systems
  • Collision Avoidance
  • Control Systems
  • Control Systems Engineering
  • Differential Equations
  • Hybrid Systems
  • Linear Systems
  • Lyapunov Functions
  • Model Predictive Control
  • Nonlinear Model Predictive Control
  • Nonlinear Systems
  • Optimization
  • Saturation
  • Standards
  • Systems Engineering
  • Unmanned Systems

Readers

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