Nobuhiro Yokoyama

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This paper addresses disturbance suppression problem for uncertain plant systems using prior disturbance data which contain some measurement errors. We tackle optimal control input design problem using Model Predictive Control (MPC) scheme in which a priori measured disturbance data are exploited. We show that if the uncertainties of the plant systems are(More)
This note addresses disturbance suppression problem for uncertain plant systems using prior disturbance data which contain some measurement errors, and gives a further result which attains lower computational complexity than our previously proposed method. Under the conditions that the plant uncertainties are assumed to be expressed by bounded but(More)
A closed-loop balanced truncation technique has been applied to an integral-type optimal servomechanism (IOS) expressed in graph-operator form of normalized right coprime factorization to produce a reduced-size state-feedback gain matrix, which is then converted into proportional-derivative (PD) gain matrices. On the other hand, the states of the integral(More)
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