Model predictive control algorithm

Badgwell, T. A., A robust model predictive control algorithm for stable nonlinear plants. Preprints of ADCHEM 97, Banff, Canada (1997). 

Testing Fault Robustness of Model Predictive Control Algorithms... 

Wright, S. J. Applying New Optimization Algorithms to Model Predictive Control. Chemical Process Control—V Proceedings. AIChE Symp Ser 316, 93 147-155 (1994). 

When thermodynamics or physics relates secondary measurements to product quality, it is easy to use secondary measurements to infer the effects of process disturbances upon product quality. When such a relation does not exist, however, one needs a solid knowledge of process operation to infer product quality from secondary measurements. This knowledge can be codified as a process model relating secondary to primary measurements. These strategies are within the domain of model-based control Dynamic Matrix Control (DMC), Model Algorithmic Control (MAC), Internal Model Control (IMC), and Model Predictive Control (MPC—perhaps the broadest of model-based control strategies). 

One important class of nonlinear programming techniques is called quadratic programming (QP), where the 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, which is heavily used in the refining industry. See the earlier subsection on model predictive control for more details. 

Model predictive control (MPC) has become widely known as dynamic matrix control (DMC) and model algorithmic control (MAC). A review of the origins of this class of techniques and their theoretical foundations is provided by Garcia et al. [10]. Many complex applications were reported at the recent IFAC Workshop [11]. 

After several years of effort, constrained model predictive control (MFC), the de facto standard algorithm for advanced control in process industries, has finally succumbed to rigorous analysis. Yet successful practical implementations of MFC were already in place almost two decades before a rigorous stability proof for constrained... 

This paper presents the application of a model based predictive control strategy for the primary stage of the freeze drying process, which has not been tackled until now. A model predictive control framework is provided to minimize the sublimation time. The problem is directly addressed for the non linear distributed parameters system that describes the dynamic of the process. The mathematical model takes in account the main phenomena, including the heat and mass transfer in both the dried and frozen layers, and the moving sublimation front. The obtained results show the efficiency of the control software developed (MPC CB) under Matlab. The MPC( CB based on a modified levenberg-marquardt algorithm allows to control a continuous process in the open or closed loop and to find the optimal constrained control. 

Industrial model predictive control (MPC) is based on algorithms that were developed many years ago. They share several common traits ... 

In the present work, we focus on the operational planning and control of integrated production/distribution systems under product demand uncertainty. For the purposes of our study and the time scales of interest, a discrete time difference model is developed. The model is applicable to networks of arbitrary structure. To treat demand uncertainty within the deterministic supply chain network model, a receding horizon, model predictive control approach is suggested. The two-level control algorithm relies on a... 

A new model-based control algorithm that interacts with an industrial batch digester was developed and has been implemented. The control predictions made by the new controller led to improved control compared with the current controller which is based... 

The use of a model predictive control (MPC) algorithm has been recently proposed by Daraoui et al. (2008) a detailed mathematical model is used (Sadikoglu and Liapis, 1997) in an algorithm that combines the internal model control (IMG) structure and the M PG framework to correct the modeling errors introduced in the model-based optimization. The manipulated variable is the shelf temperature and the goal is the minimization of the drying time constraints on the manipulated and on the controlled variables can be easily taken into account in the optimization algorithm. 

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