Faculty Publications


A new approach to implementing a PLC-based model predictive controller for application in industrial food processes

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ASEE Annual Conference Proceedings

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Model Predictive Control (MPC) is an advanced control strategy for improving the control of processes that display relatively large variations in system controlled output values in comparison to the system's control set point, for processes with appreciable process variable interactions, and for systems that display a large amount of process deadtime and/or system disturbances This paper reports the implementation of MPC techniques directly on a programmable logic controller (PLC) rather than on a personal computer (PC) for an industrial sugar cooking process. This study implemented and evaluated three PC-based, commercial MPC technologies for the sugar cooking process, and a new model state feedback implementation directly on Rockwell Automation's ControlLogix® PLC. A standard proportional-integral-derivative (PID) control implementation was used as a baseline for comparing the MPC strategies. The comparative analysis focused on the dynamic response of each strategy at startup, including both temperature rise time and overshoot, and the steady-state disturbance rejection capabilities of each strategy. The test results showed that the MPC strategies controlled the sugar cooking process better than the traditional PID control method in regards to the target parameters. The tests also showed that the PLC-based MPC strategy was comparable to the PC-based commercial MPC applications. This strategy has several benefits such as requiring no external hardware, software, and communications protocols, which may result in a less expensive implementation than the commercial MPC strategies. This is of particular interest for implementation in an educational setting due to the lower cost, use of standard laboratory equipment, and relevance to Electrical Engineering and Engineering Technology curriculum.

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