Implementation of model predictive control using real-time multiprocessing computing

Abstract

This work addresses the issue of finding the programming procedure that results to the fastest implementation of the core calculations of the model predictive control (MPC) algorithms that are amenable to parallel processing on a real-time multiprocessing system. Three concurrent programming procedures were considered for the MPC implementation, each one consisting of a number of tasks configured to a linear array, mesh and tree parallel architectures. Both theoretical analysis and measurements, taken from running these procedures on a four-processor computer platform, indicate that the procedure of the linear array architecture presents the best speed-up ratio.