\r\nthat arises in many fields. Burgers’ equation is a fundamental

\r\nequation for several flow phenomena such as traffic, shock waves,

\r\nand turbulence. The optimal feedback control method, so-called

\r\nmodel predictive control, has been proposed for Burgers’ equation.

\r\nHowever, the model predictive control method is inapplicable to

\r\nsystems whose all state variables are not exactly known. In practical

\r\npoint of view, it is unusual that all the state variables of systems are

\r\nexactly known, because the state variables of systems are measured

\r\nthrough output sensors and limited parts of them can be only

\r\navailable. In fact, it is usual that flow velocities of fluid systems

\r\ncannot be measured for all spatial domains. Hence, any practical

\r\nfeedback controller for fluid systems must incorporate some type of

\r\nstate estimator. To apply the model predictive control to the fluid

\r\nsystems described by Burgers’ equation, it is needed to establish

\r\na state estimation method for Burgers’ equation with limited

\r\nmeasurable state variables. To this purpose, we apply unscented

\r\nKalman filter for estimating the state variables of fluid systems

\r\ndescribed by Burgers’ equation. The objective of this study is to

\r\nestablish a state estimation method based on unscented Kalman filter

\r\nfor Burgers’ equation. The effectiveness of the proposed method is

\r\nverified by numerical simulations.","references":null,"publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 143, 2018"}