Applicability limits of model-based predictive control algorithms under uncertain control object dynamics

It is noted that advanced process control (APC) comprises a wide variety of mathematical methods and software algorithmic tools, application of which allows to increase the production profitability through more effective compensation of external impact on the process, automatic selection and maintenance of an optimal mode. It is also noted that model predictive control (MPC) of the multi-loop objects has been the mainstream of APC over the past 30 years. The robustness is examined of MPC algorithms with regard to model accuracy. The term «robustness» is understood as the ability of attaining the control aim under conditions of uncertainty. Control applicability criterion is formulated allowing to conclude about the correctness of the control algorithm. The model in the study is specified in a form of a transfer function of a linear object with time delay. Boundary conditions are determined for transfer function parameters, the violation of which leads to the control object destabilization or to the sluggish control. The results are given of the numerical calculation of 3D-domains of the model parameters, within which the control applicability criterion is met. The validity range of the results obtained is given.