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Model Predictive Control (MPC) originated in the late seventies and has developed considerably since then. There are many applications of predictive control successfully in use at present time, mostly in process industry but also applications to the control of a diversity of processes (e.g. robot manipulators, servo drives, clinical anaesthesia). The good performance of these applications shows the capacity of the MPC to achieve highly efficient control systems. MPC has become the accepted standard for complex constrained multivariable control problems in the process industries. The term Model Predictive Control does not designate a specific control strategy but a very ample range of control methods which make an explicit use of a model of the process to obtain the control signal by minimizing an objective function. The process model is used to predict the future process output at current sampling instant. This prediction capability allows solving optimal control problems online, where tracking error, namely the difference between the predicted process output and the desired reference, is minimized over a future horizon, possibly subject to constraints on the manipulated inputs and outputs. Thus, MPC requires online real-time optimization, which can be computationally quite a complex task. The different strategies of MPC can use various models to represent the relationship between the outputs and the measurable inputs, some of which are manipulated variables and others can be considered to be measurable disturbances. Most control theory and tools have been developed for systems whose evolution is described by smooth linear or non-linear state transition functions resulting, for example, from differential or difference equations. In many applications, however, the system includes discrete components, such as on/off switches or valves, gears or speed selectors. Discrete characteristics are also often introduced by the control system or by the specifications expressed as a series of if-then-else rules. Such systems are commonly referred to as hybrid systems. Mixed Logical Dynamical (MLD) systems is a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operating constraints. This project will focus on model predictive control of these systems. Two main open problems in model predictive control that need solutions are: (i) formidable on-line computational effort, which limits applicability of MPC to the relatively slow and/or small problems and (ii) robustness of MPC to the process model uncertainty and noise. Alleviation of these two problems will be the main research topics of this project. Direct project objectives are development of computationally efficient MPC algorithms and development of systematic techniques for obtaining robust MPC. These will make this profound control concept suitable for wide range of applications, ranging from processes with very fast dynamics to large-scale processes.
Short description of the task performed by Croatian partner
Ovo web sjedište koristi tehnologiju "kolačića" (eng. cookie) da bi se korisnicima prikazao dinamičan i personaliziran sadržaj, te su oni nužni za ispravan rad. Ako nastavite pregledavati ove stranice, kolačići će biti korišteni u suradnji s vašim preglednikom Weba.
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