This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology. A general MPC control algorithm is presented, and approaches taken b...

Matrix-producing carcinoma (MPC) is extremely rare. Limited reports have described the cytological aspects of MPC. Herein, we present 2 cases of MPC, both of which showed ring-enhancement on magnetic resonance imaging (MRI) and chondromyxoid matrix on cytological specimens. In these cases, the diagnosis of MPC was preoperatively suspected. Recognizing extracellular matrix as chondromyxoid matri...

In this paper we develop a Model Predictive Controller (MPC) for regulation of a cement mill circuit. The MPC uses soft constraints (soft MPC) to robustly address the large uncertainties present in models that can be identified for cement mill circuits. The uncertainties in the linear predictive model of the cement mill circuit stems from large variations and heterogeneities in the feed materia...

Most standard MPC implementations partition the plant into several units and apply MPC individually to these units. It is known that this strategy can lead to sub-optimal plant-wide control performance, especially if the units interact strongly. This paper tackles the problem of achieving optimal control performance in plants with such an MPC structure. A modeling framework, geared for use in M...

The objective of this study was to investigate the relationship between pharmacokinetic and pharmacodynamic parameters, on the basis of the mutant prevention concentration (MPC) concept, and the emergence of resistant mutants of Streptococcus pneumoniae to fluoroquinolone antibacterials. Some clinical isolates with various MIC and MPC values of moxifloxacin and levofloxacin were exposed under c...

Abstract: Model predictive control (MPC) has been widely studied for regulating frequency in stand-alone microgrids (MGs), owing to the advantages of MPC such as fast response and robustness against the parameter uncertainties. Understanding the impacts of system parameters on the control performance of MPC could be useful for the designing process of the controller to achieve better performanc...

this article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (mpc) of a chemical plant. a combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (mimo) process with time delays.  an optimization procedure for a neural mpc algorithm based on this model is then developed. the p...

We interpret economic MPC as a scheme that trades off economic performance and stability. We use this notion to design an economic MPC controller that exploits the inherent robustness of a stable auxiliary MPC controller to enhance economic performance. Specifically, we incorporate a flexible stabilizing constraint to the economic MPC formulation that preserves stability of the auxiliary contro...

Model predictive control (MPC) includes a receding-horizon control techniques based on the process model for predictions of the plant output. Since late 1970’s several MPC approaches have been reported in the literature. Selection of the most appropriate MPC approach depend on the specific problem. In this paper, discrete time MPC is applied to a inverted pendulum system coupled to a cart. The ...

Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses linear or nonlinear discrete-time models. Recently, we have extended MPC to a class of discrete event systems that can be described by a model that is “linear” in the max-plus algebra. In our previous work we have considered MPC for the time-invariant case. In this paper we con...