A Toolchain for Solving Dynamic Optimization Problems Using Symbolic and Parallel Computing

Abstract

Significant progresses in developing approaches to dynamic optimization have been made. However, its practical implementation poses a difficult task and its realtime application such as in nonlinear model predictive control (NMPC) remains challenging. A toolchain is developed in this work to relieve the implementation burden and, meanwhile, to speed up the computations for solving the dynamic optimization problem. To achieve these targets, symbolic computing is utilized for calculating the first and second order sensitivities on the one hand and parallel computing is used for separately accomplishing the computations for the individual time intervals on the other hand. Two optimal control problems are solved to demonstrate the efficiency of the developed toolchain which solves one of the problems with approximately 25,000 variables within a reasonable CPU time.