A new Takagi-Sugeno-Kang model-based stabilizing explicit MPC formulation: An experimental case study with implementation embedded in a PLC

This manuscript presents a new fuzzy approach applied to Model Predictive Control (MPC). We propose to re-interpret the table of IF-THEN rules from an explicit MPC solution as an expert system. The first contribution of this work is a modification in the Takagi-Sugeno-Kang (TSK) structure that allows modeling this table of IF -THEN rules without approximation and maintaining the performance of the explicit MPC solution. The second contribution is a new MPC formulation that guarantees closed-loop system stability by combining a one-layer optimization, a suitable Jordan decomposition-based state-space model, and relaxed terminal constraints. These proposals were implemented in a general-purpose Programmable Logic Controller, following the IEC-61131-3 standard, and applied to control the speed of a physical DC motor. The new modified TSK method and the stabilizing MPC formulation showed low-level computational effort and the desired control performance. The results indicate the potential application in standard automation systems available at industrial process plants.

Automated summary

This manuscript presents a new fuzzy approach applied to Model Predictive Control (MPC). The first contribution of this work is a modification in the Takagi-Sugeno-Kang (TSK) structure that allows modeling IF-THEN rules without approximation. The second contribution is a new MPC formulation that guarantees system stability by combining optimization, state-space model, and terminal constraints. These methods were implemented on a Programmable Logic Controller and applied to control a physical DC motor.