Model Predictive Control in the Process Industry
Model Predictive Control in the Process Industry
Newton-KKT interior-point methods for indefinite quadratic programming
Computational Optimization and Applications
Functional DIF for Rapid Prototyping
RSP '08 Proceedings of the 2008 The 19th IEEE/IFIP International Symposium on Rapid System Prototyping
IEEE Transactions on Signal Processing
The explicit linear quadratic regulator for constrained systems
Automatica (Journal of IFAC)
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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.