<p>Both actuator faults and time delays degrade the performance of control systems. Although fault-tolerant mechanisms are commonly used in advanced control systems, no results are available in investigating the adaptive tracking problem of stochastic nonlinear time-delay systems in the presence of Markovian jump actuator faults. After establishing some mathematical fundamentals for stochastic differential delayed equations with multi-Markovian switching, this issue is tackled in this article, by proposing a novel adaptive backstepping fault-tolerant controller. Uncertainties caused by random actuator faults, unknown time-varying delays, the Wiener noise of unknown covariance as well as the unknown plant parameters are handled skillfully in a unified stochastic framework. By constructing a suitable Lyapunov-Krasovskii functional, it is proved that all closed-loop signals are bounded in probability, and the tracking error can converge into an arbitrarily small residual set in the sense of mean quartic value. In addition, the range of reference signals is greatly enlarged by comparison with the conventional backstepping controller. Two simulation examples are presented to illustrate the proposed theoretical findings.</p>

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Adaptive Tracking for Stochastic Nonlinear Time-Delay Systems Subject to Markovian Jump Actuator Faults

  • Jiao-Yang Zhang,
  • Huijin Fan,
  • Xinpeng Fang,
  • Lei Liu,
  • Bo Wang

摘要

Both actuator faults and time delays degrade the performance of control systems. Although fault-tolerant mechanisms are commonly used in advanced control systems, no results are available in investigating the adaptive tracking problem of stochastic nonlinear time-delay systems in the presence of Markovian jump actuator faults. After establishing some mathematical fundamentals for stochastic differential delayed equations with multi-Markovian switching, this issue is tackled in this article, by proposing a novel adaptive backstepping fault-tolerant controller. Uncertainties caused by random actuator faults, unknown time-varying delays, the Wiener noise of unknown covariance as well as the unknown plant parameters are handled skillfully in a unified stochastic framework. By constructing a suitable Lyapunov-Krasovskii functional, it is proved that all closed-loop signals are bounded in probability, and the tracking error can converge into an arbitrarily small residual set in the sense of mean quartic value. In addition, the range of reference signals is greatly enlarged by comparison with the conventional backstepping controller. Two simulation examples are presented to illustrate the proposed theoretical findings.