Model predictive control dynamic programming

One important class of nonlinear programming techniques is called quadratic programming (QP), where me objective function is quadratic and the constraints are linear. While the solution is iterative, it can be obtained quickly as in linear programming. This is the basis for the newest type of constrained multivariable control algorithms called model predictive control. The dominant method used in the refining industry utilizes the solution of a QP and is called dynamic matrix con-... 

The model predictive control used includes all features of Quadratic Dynamic Matrix Control [19], furthermore it is able to take into account soft output constraints as a non linear optimization. The programs are written in C++ with Fortran libraries. The manipulated inputs (shown in cm Vs) calculated by predictive control are imposed to the full nonlinear model of the SMB. The control simulations were made to study the tracking of both purities and the influence of disturbances of feed flow rate or feed composition. Only partial results are shown. 

Hence, the aforementioned enterprise-wide model is extended to a stochastic program that takes exogenous uncertainty into account, namely, demand, price and interest rates variability. To tackle the resulting problem, scenario-based multi-stage stochastic mixed integer modeling techniques are applied. Then, the stochastic model is introduced into a model predictive controller to capture the dynamics of SC processes and their environment. The ensuing model can be used as a support tool... 

A dynamic model for on-line estimation and control of a fixed bed catalytic reactor must be based on a thorough experimental program. It must be able to predict the measured experimental effects of the variation of key variables such as jacket temperature, feed flow rate, composition and temperature on the dynamic behaviour of the reactor this, in turn, requires the knowledge of the kinetic and "effective" transport parameters involved in the model. 

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