On December 2, we organized a scientific seminar on “Self-Tunable Approximated Explicit Model Predictive Control”. The scientific seminar was led by Lenka Galčíková, a PhD. student at the Institute of Information Engineering, Automation and Mathematics FCFT STU. The scientific seminar was organised in the framework of the FrontSeat project, as part of the seminar series on “Research Seminar on Smart Cybernetics”.
In many practical applications, it is often beneficial or even necessary to modify the controller parameters according to current operating conditions. This work focuses on this necessity and proposes the idea of a self-tuning technique for the approximated explicit MPC. The tuning of approximated explicit model predictive control is performed through linear interpolation between two optimal explicit model predictive controllers. The explicit controllers are constructed based on different input penalty matrices – the upper and lower bound on penalty matrix R. An idea of self-scaling of penalty matrix R is presented. The aggressiveness of the controller is adjusted whenever a change of reference value occurs. The proposed idea of this online self-tuning of the explicit controller is simulated on a system of a laboratory heat exchanger. As the aggressiveness of the approximated controller is adjusted during control, improvement in control performance is achieved compared to the explicit controllers utilizing only the lower or the upper bound on penalty matrix R during the entire control. In addition, the proposed method also leads to decreased energy consumption associated with the volume of the heating medium.
This project has received funding from the European Union’s Horizon under grant no. 101079342 (Fostering Opportunities Towards Slovak Excellence in Advanced Control for Smart Industries).