Comparison of LQR with MPC in the adaptive stabilization of a glass conditioning process using soft-sensors for parameter identification and state observation

The paper presents the comparison of two different continuous-time adaptive control strategies applied to the temperature stabilization of molten glass during conditioning. Both control methods include on-line linear continuous-time model parameter identification using a nonstandard procedure based on the modulating functions method. The related control task is of great practical importance because it directly affects the quality of manufactured glass containers. The molten glass temperature must be stabilized with accuracy of about 1C∘ which can be very difficult. At the core of this work, the synthesis of a nonstandard adaptive control procedure is described that consists of a linear quadratic regulator (LQR) being fed with process parameters and state estimates. These new state estimates are generated with a special transform and reconstructed by a special type of modulating function state observer consisting of two modulating function based soft-sensors which rely on a continuous-time model. However, an equally important issue of this investigation is the efficiency and accuracy of the algorithm. To this end, the described stabilization method will be compared with a standard continuous-time model predictive control (MPC) approach that was used in the authors' previous research on the continuous molten glass temperature stabilization in a single glass forehearth zone. Simulation results based on experimental calibration data are presented and compared for these two approaches. It turns out that the first method with LQR is simpler than the MPC approach while maintaining the same level of accuracy and quality of control.