A formulation of nonlinear model predictive control using automatic differentiation

An efficient algorithm is developed to alleviate the computational burden associated with nonlinear model predictive control (NMPC). The new algorithm extends an existing algorithm for solutions of dynamic sensitivity from autonomous to non-autonomous differential equations using the Taylor series and automatic differentiation (AD). A formulation is then presented to recast the NMPC problem as a standard nonlinear programming problem by using the Taylor series and AD. The efficiency of the new algorithm is compared with other approaches via an evaporation case study. The comparison shows that the new algorithm can reduce computational time by two orders of magnitude.