From offline to onboard system solution for a control sequence optimization problem

The control sequence optimization problem is difficult to solve due to its high nonlinearity, various constraints and the possible changes in the sequence of comprising elements at any instant of time. The optimization of train trip running profile is a typical control sequence optimization problem, whose optimization object is to minimize the energy consumption as well as the time deviation under various constraints. Engineers always have to face the trade-off between the optimization performance and calculation time for an onboard control system for such problems. This paper mainly proposed a framework of an offline to onboard system solution for control sequence optimization problems, specifically using on the train trip profile optimization problems. The framework choose the parameter-decision tree solution for the onboard control system, and then a series of offline procedures including sequence mining, optimal computation, and machine learning is proposed for getting the parameter-decision tree. The framework inherits the good optimization performance of offline systems, as well as guaranteed the onboard calculation time for real-time control. Performance on using such a framework for solving train trip profile optimization problems is shown in the literature, which shows the potentials of using such frameworks on solving related control sequence optimization problems.