Methods for efficient implementation of Model Predictive Control on multiprocessor systems

Model Predictive Control (MPC) has been used in a wide range of application areas including chemical engineering, food processing, automotive engineering, aerospace, and metallurgy. An important limitation on the application of MPC is the difficulty in completing the necessary computations within the sampling interval. Recent trends in computing hardware towards greatly increased parallelism offer a solution to this problem. This paper describes modeling and analysis tools to facilitate implementing the MPC algorithms on parallel computers, thereby greatly reducing the time needed to complete the calculations. The use of these tools is illustrated by an application to a class of MPC problems.