Robust control of nonlinear systems with parametric uncertainty

Qian Wang, Robert F. Stengel

Research output: Contribution to journalArticlepeer-review

86 Scopus citations

Abstract

Probabilistic robustness analysis and synthesis for nonlinear systems with uncertain parameters are presented. Monte Carlo simulation is used to estimate the likelihood of system instability and violation of performance requirements subject to variations of the probabilistic system parameters. Stochastic robust control synthesis searches the controller design parameter space to minimize a cost that is a function of the probabilities that design criteria will not be satisfied. The robust control design approach is illustrated by a simple nonlinear example. A modified feedback linearization control is chosen as controller structure, and the design parameters are searched by a genetic algorithm to achieve the tradeoff between stability and performance robustness.

Original languageEnglish (US)
Pages (from-to)1591-1599
Number of pages9
JournalAutomatica
Volume38
Issue number9
DOIs
StatePublished - Sep 2002

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Genetic algorithm
  • Input-to-state stability
  • Monte Carlo simulation
  • Stochastic robustness

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