Planning for mechatronics systems—Architecture, methods and case study

Mechatronic systems are a relatively new class of technical systems. The integration of electro-mechanical systems with hard- and software enables systems that adapt to changing operation conditions and externally defined objective functions. To gain superior system performance from this ability, sophisticated decision making processes are required. Planning is an ideal method to integrate long-term considerations beyond the time horizon of classical controlled systems into the decision making process. Unfortunately, planning employs discrete models, while mechatronic systems or controlled systems in general emphasize the time continuous behavior of processes. As a result, deviations of the actual behavior during the execution from the planned behavior plan cannot be entirely avoided. We introduce a hybrid planning architecture, which combines planning and learning from artificial intelligence with simulation techniques to optimize the general system behavior. The presented approach is able to handle the inevitable deviations during plan execution, and thus maintains feasibility and quality of the created plans.