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Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. In recent years it has also been used in power system balancing models and in power electronics. Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification.
Event-based control is an alternative to traditional control where new measurements are sampled only if critical events occur. This not only allows to reduce the control effort but it satisfies nowada
We propose a model predictive control approach to path-following problems of constrained nonlinear systems. We directly consider input and state constraints. Furthermore, we introduce an extended corr
IEEE2009
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In many control problems the measurement instances might not be available in a periodically-equally-distributed way and/or the same might occur to the control signals. Moreover, due to the sensor eval