Abstract: A model-based control of an industrial process using a merged MLD system model is provided for the estimation and subsequent control of the process. An optimization of an objective function is performed. The objective function includes a difference between an observed quantity and an output variable of a Mixed Logical Dynamic (MLD) system model of the process. The optimization is performed as a function of state variables of the MLD system model, over a number of time steps in the past, and subject to constraints defined by the MLD system model's dynamics. The optimizing values of the state variables are retained as estimated initial states for subsequent control of the process in a model-based manner including the same MLD system model. The single MLD system model is a combination or merger of individual MLD subsystem models representing the sub-processes of the process, and may be elaborated during a customization step.

Abstract: A method is disclosed for estimating states of a power electronic system, the system having a converter circuit. An exemplary method includes varying a system state vector x(k) and a system state vector x(k+1) for each of sampling times k=?N+1 to k=0 in such a manner that a sum formed by an addition of a first vector norm obtained by subtracting a first system model function f(x(k), u(k)) from the system state vector x(k+1), and another vector norm obtained by subtracting a second system model function g(x(k), u(k)) from the output variable vector y(k), becomes minimal over the sampling times k=?N+1 to k=0. A desired system state vector x(k) at the sampling time k=0 can then be selected.

Abstract: A model-based control of an industrial process using a merged MLD system model is provided for the estimation and subsequent control of the process. An optimization of an objective function is performed. The objective function includes a difference between an observed quantity and an output variable of a Mixed Logical Dynamic (MLD) system model of the process. The optimization is performed as a function of state variables of the MLD system model, over a number of time steps in the past, and subject to constraints defined by the MLD system model's dynamics. The optimizing values of the state variables are retained as estimated initial states for subsequent control of the process in a model-based manner including the same MLD system model. The single MLD system model is a combination or merger of individual MLD subsystem models representing the sub-processes of the process, and may be elaborated during a customization step.

Abstract: A method is disclosed for estimating states of a power electronic system, the system having a converter circuit. An exemplary method includes varying a system state vector x(k) and a system state vector x(k+1) for each of sampling times k=?N+1 to k=0 in such a manner that a sum formed by an addition of a first vector norm obtained by subtracting a first system model function f(x(k), u(k)) from the system state vector x(k+1), and another vector norm obtained by subtracting a second system model function g(x(k), u(k)) from the output variable vector y(k), becomes minimal over the sampling times k=?N+1 to k=0. A desired system state vector x(k) at the sampling time k=0 can then be selected.