Dataflow-based implementation of model predictive control

Model predictive control (MPC) has been used in a wide range of application areas including chemical engineering, food processing, automotive engineering, aerospace, and metallurgy. MPC is often computation intensive, which limits the class of systems to which it can be applied and the performance criteria it can use. This paper describes a general framework called reactive, control-integrated dataflow modeling for analyzing and improving the algorithms used for MPC and their hardware implementations. The utility of the framework is demonstrated by applying it to the Newton-KKT algorithm. The results show significant reductions in computation time for test cases.