Andrea Šušak


  • Predictive control of an industrial process by PLC, master's thesis, 2020


Predictive control of an industrial process by PLC


The optimization of industrial processes potentially brings significant savings of energy, time and raw materials in the production process and is the basis for sustainable production as present trend. In recent years, the requirements for the quality of automatic control in the process industries increased significantly due to the increased complexity of the plants and sharper specifications of product quality. Furthermore, increasing problems with operating constraints consequently lead to the necessity to develop more sophisticated control strategies. Model based control techniques were developed to obtain tighter control for such applications. Predictive control algorithms have great acceptance in the process industry because of its ability to handle multivariable and constrained systems. Since the presence of constraints greatly increases the computational cost of solving these problems, predictive control algorithms were initially used in slow processes. However, due to the increasing processing power and developed mathematical algorithms, predictive control has found a wide range of control systems. This master thesis describes the process of cold rolling process which plays an important role in metal processing due to the requirements for high quality rolled products in the aerospace, automotive and other industries. A mathematical model of the described process was developed, and a generalized predictive control algorithm was synthesized for it, treating the disturbance as a controlled auto-regressive model with integrated moving average (CARIMA). Frstly, GPC control is simulated using Matlab/Simulink and then, the control algorithm is implemented on PLC using Simatic Step 7 tool.

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Keywords: central process industries, model-predictive control, GPC, cold rolling, AGC system, HGC system, PLC