\r\ngeneralized class of systems than a class of explicit systems. To

\r\nestablish a control method for such a generalized class of systems, we

\r\nadopt model predictive control method which is a kind of optimal

\r\nfeedback control with a performance index that has a moving

\r\ninitial time and terminal time. However, model predictive control

\r\nmethod is inapplicable to systems whose all state variables are not

\r\nexactly known. In other words, model predictive control method is

\r\ninapplicable to systems with limited measurable states. In fact, it

\r\nis usual that the state variables of systems are measured through

\r\noutputs, hence, only limited parts of them can be used directly. It is

\r\nalso usual that output signals are disturbed by process and sensor

\r\nnoises. Hence, it is important to establish a state estimation method

\r\nfor nonlinear implicit systems with taking the process noise and

\r\nsensor noise into consideration. To this purpose, we apply the model

\r\npredictive control method and unscented Kalman filter for solving

\r\nthe optimization and estimation problems of nonlinear implicit

\r\nsystems, respectively. The objective of this study is to establish a

\r\nmodel predictive control with unscented Kalman filter for nonlinear

\r\nimplicit systems.","references":null,"publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 139, 2018"}