Fast QP algorithms for predictive control

Fast QP (quadratic programming) algorithms for predictive control are investigated. It is shown how it is possible to reduce the computational complexity for the solution of the CLQ (constrained linear quadratic) regulation problem with arbitrary linear constraints, exploiting the active set method in combination with dynamic programming and triangularization techniques. The developed algorithmic tools can be used to increase the sampling rate in predictive control implementation.