The usual direct optimization approaches can hardly obtain a “good” numerical solution for the dynamic optimization(DO)problem with control switching structure if the chosen control discretization does not properly reflect the switching structure.In this paper

by reformulating the DO problem as a multi-stage optimization problem

a second exploitation strategy is proposed for solving this problem.The potential control structure can be revealed from the first solution generated by the usual optimization approaches.Subsequently

the DO problem is partitioned as several stages

with each stage corresponding to a particular control arc.A control vector parameterization approach is applied to convert the multi-stage DO problem to a static nonlinear programming(NLP)problem.The control profiles and stage lengths act as decision variables.Based on the Pontryagin maximal principle

a multi-stage adjoint system is constructed to calculate the gradients required by the NLP solvers.Two examples are studied to demonstrate the effectiveness of this strategy.