Model predictive control for constrained discrete time systems: An Integrated perturbation analysis and sequential quadratic programming approach

Further improvements in computational efficiency of numerical optimization algorithms is a promising venue to extend the applicability of model predictive control (MPC) to broader classes of embedded systems with fast dynamics and limited computing resources. Along these lines, we develop a novel numerical optimization algorithm based on integrated perturbation analysis and sequential quadratic programming (IPA-SQP), which exploits special structure of the optimization problem and complementary features of perturbation analysis and SQP methods, to improve computational efficiency in general MPC problems with mixed state and input constraints. An example is reported to illustrate the reduction in on-line computing time achieved with IPA-SQP approach.