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Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—potentially constrained—optimization problem, MPC determines the control law implicitly. This shifts the effort for the design of a controller towards modeling of the to-be-controlled process. Since such models are available in many fields of engineering, the initial hurdle for applying control is deceased with MPC. Its implicit formulation maintains the physical understanding of the system parameters facilitating the tuning of the controller. Model-based predictive control (MPC) can even control systems, which cannot be controlled by conventional feedback controllers. With most of the theory laid out, it is time for a concise summary of it and an application-driven survey. This review article should serve as such. While in the beginnings of MPC, several widely noticed review paper have been published, a comprehensive overview on the latest developments, and on applications, is missing today. This article reviews the current state of the art including theory, historic evolution, and practical considerations to create intuitive understanding. We lay special attention on applications in order to demonstrate what is already possible today. Furthermore, we provide detailed discussion on implantation details in general and strategies to cope with the computational burden—still a major factor in the design of MPC. Besides key methods in the development of MPC, this review points to the future trends emphasizing why they are the next logical steps in MPC.

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In glass compression molding, most current modeling approaches of temperature‐dependent viscoelastic behavior of glass materials are restricted to thermo‐rheologically simple assumption. This research conducts a detailed study and demonstrates that this assumption, however, is not adequate for glass molding simulations over a wide range of molding temperatures. In this paper, we introduce a new method that eliminates the prerequisite of relaxation functions and shift factors for modeling of the thermo‐viscoelastic material behavior. More specifically, the temperature effect is directly incorporated into each parameter of the mechanical model. The mechanical model parameters are derived from creep displacements using uniaxial compression experiments. Validations of the proposed method are conducted for three different glass categories, including borosilicate, aluminosilicate, and chalcogenide glasses. Excellent agreement between the creep experiments and simulation results is found in all glasses over long pressing time up to 900 seconds and a large temperature range that corresponds to the glass viscosity of log (η) = 9.5 – 6.8 Pas. The method eventually promises an enhancement of the glass molding simulation.

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Nonisothermal glass molding has recently become a promising technology solution for the cost‐efficient production of complex precision glass optical components. During the molding process, the glass temperature and its temperature distribution have crucial effects on the accuracy of molded optics. In nonisothermal molding, the glass temperature is greatly influenced by thermal contact conductance because there is a large temperature difference between the glass and mold parts. Though widely agreed to be varied during the molding process, the contact conductance was usually assumed as constant coefficients in most early works without sufficient experimental justifications. This paper presents an experiment approach to determine the thermal contact coefficient derived from transient temperature measurements by using infrared thermographic camera. The transient method demonstrates a beneficially short processing time and the adequate measurement at desirable molding temperature without glass sticking. Particularly, this method promises the avoidance of the overestimated contact coefficients derived from steady‐state approach due to the viscoelastic deformation of glass during the inevitably long period of holding force. Based on this method, the dependency of thermal contact conductance on mold surface roughness, contact pressure, and interfacial temperature ranging from slightly below‐to‐above glass transition temperature was investigated. The results reveal the dominance of interfacial temperature on the contact conductance while the linear pressure‐dependent conductance with an identical slope observed for all roughness and mold temperatures. The accurate determination of the contact heat transfer coefficients will eventually improve the predictions of the form accuracy, the optical properties, and possible defects such as chill ripples or glass breakage of molded lenses by the nonisothermal glass molding process.