A MRAS Based Model Predictive Control for Multi-Leg Based Multi-Drive System Used in Hot Rolling Mill Applications

Abstract

In this paper, a multi-leg based multi-drive configuration is presented to be used in multi AC drive applications such as the hot rolling mills. The proposed configuration enjoys compactness, fewer semiconductors, and lower drive cost compared with conventional topology, making it a promising approach. In a conventional rolling mill stand, an active front-end (AFE) rectifier and two inverters are required for grid-side and motor-side connections. However, in the proposed configuration, all converters are unified using a multi-leg structure. To improve the operational performance of the drive, a model predictive control (MPC) is designed. In this approach, an individual cost function for every output, along with a comprehensive cost function containing all control objectives for the overall system, is defined. By obtaining the valid and invalid switching states of the proposed converter, the defined multi-objective cost function is minimized to find the most optimum switching states in each sampling time. In addition, to improve the robustness of the proposed multi-drive system, a model reference adaptive system (MRAS) is designed to estimate rotor speed and stator resistance. The high-performance capability of the proposed multi-drive system is evaluated in both steady and dynamic states using MATLAB/Simulink software and compared with a conventional configuration. According to simulation results, it is deduced that the independent control of the top and bottom motors in a rolling mill stand can be guaranteed using the proposed low-cost sensor-less drive.

Keywords

• Cost function
• induction motor
• model predictive control
• robustness
• model reference adaptive system
• multi-drive system