Abstract:

Model predictive control has been originally developed for chemical process control, where plants are expensive, have slow dynamics, and a large number of inputs and outputs. Furthermore, in chemical process control each control system is usually deployed to a single plant, and hence can be specifically tuned. In recent years there has been a growing interest towards MPC in other industries, such as automotive, factory automation, and aerospace, where the plants have faster dynamics, fewer inputs and outputs, reduced costs, and each controller is deployed to a large number of plants, i.e., it is deployed in large volumes. These applications also presents several classes of nonlinearities. While there are several benefits for using MPC in these industries, the difference in the plant characteristics and in production volume targets pose several challenges to the widespread use of MPC that are still partially unsolved. In this paper we discuss the benefits of MPC in large volumes industries, by using examples from automotive, aerospace, and mechatronics that also present several specific nonlinearities that can be efficiently handled by MPC.We then discuss the unsolved challenges for these application domains, and the related ongoing research.