We propose a novel method for constructing probabilistic robust disturbance rejection control for uncertain systems in which a scenario optimization method is used to deal with the nonlinear and unbounded uncertainties. For anti-disturbance, a reduced order disturbance observer is considered and a state-feedback controller is designed. Sufficient conditions are presented to ensure that the resulting closed-loop system is stable and a prescribed $ H_{\infty} $ performance index is satisfied. A numerical example is presented to illustrate the effectiveness of the techniques proposed and analyzed.
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State trajectory of a-posteriori Monte-Carlo analysis
Estimation of disturbance
Trajectory of controlled output