A Survey of Parallel Implementations for Model Predictive Control

Abstract

Model Predictive Control (MPC) has its reputation since it can handle multiple inputs and outputs with consideration to constraints. However, this comes at the cost of high computational complexity, which limits MPC to slow dynamic systems. This paper provides an overview of the available methods to accelerate the MPC process. Various parallel computing approaches using different technologies were proposed to speed up the execution of MPC, some of these approaches are focused on building dedicated hardware for MPC using field programmable arrays (FPGA), and others are focused on parallelizing MPC computation using multi-core processors (CPUs) and many-core processors (GPUs). The focus of this survey is to review the available methods for accelerating MPC process. A brief introduction to the theory of MPC is provided first followed by a description of each approach. A comparison between the different methods is presented in terms of complexity and performance followed by a valid application for each approach. Finally, this paper discusses the challenges and requirements of MPC for future applications.