Abstract
The problem of robust model predictive control is studied for the singular systems with norm-bounded uncertainties when the states of controlled systems are unmeasurable. By using of the LMIs, the infinite time domain “min-max” optimization problems are converted into convex optimization problems. By constructing the Lyapunov function with the error term, the sufficient conditions for the existence of robust model predictive control are derived and expressed as linear matrix inequalities. The robust stability of the closed-loop singular systems is guaranteed by the proposed design method, and the singular systems are regular and the impulse-free. A simulation example illustrates the efficiency of this method.
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