A simultaneous strategy for dynamic optimization based on symbolic derivation

A novel simultaneous strategy for solving dynamic optimization problems (DOPs), which obtains symbolic derivation of the original problems before discretization, is studied in this work. In this strategy, the discretized nonlinear program (NLP) can be separated into two parts, named model-part and method-part. The model-part is determined by the original DOPs, and the method-part is decided by a collocation method. A solution framework based on symbolic computation is developed to discretize and solve the DOPs. All of these concepts are illustrated with two dynamic optimization examples.