A Robust Nonlinear Model Reference Adaptive Control for Disturbed Linear Systems: An LMI Approach

Roberto Franco, Hector Rios, Alejandra Ferreira de Loza, Denis Efimov

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper a robust nonlinear Model Reference Adaptive Control (MRAC) is proposed for disturbed linear systems, i.e., linear systems with parameter uncertainties, and external time-dependent perturbations or nonlinear unmodeled dynamics matched with the control input. The proposed nonlinear control law is composed of two nonlinear adaptive gains. Such adaptive gains allow the control to counteract the effects of some perturbations and nonlinear unmodeled dynamics ensuring asymptotic convergence of the tracking error to zero, and the boundedness of the adaptive gains. The nonlinear controller synthesis is given by a constructive method based on the solution of Linear Matrix Inequalities (LMIs). Besides, the simulation results show that, due to the nonlinearities, the rate of convergence of the proposed algorithm is faster than the provided by a classic MRAC.

Original languageEnglish
JournalIEEE Transactions on Automatic Control
DOIs
StateAccepted/In press - 2021

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Adaptation models
  • Adaptive control
  • Convergence
  • Linear systems
  • Model Reference Adaptive Control
  • Nonlinear Control
  • Perturbation methods
  • Robust Control
  • Simulation
  • Uncertain Linear Systems
  • Uncertain systems

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