Proportional-integral-differential controllers (PID controllers) are one of the most widespread controller architectures and are used in a large number of technical systems [1]. When operating highly dynamic test benches to investigate the system behaviour of powertrain components with rotating movements, the controllers are often used as speed controllers. In order to reproduce the complex phenomena in such a powertrain as accurately as possible, the high dynamics of the test bench is required. This means that the test bench must be able to vary the speed highly dynamically and at the same time have the least possible unwanted influences on the system under investigation. This enables the system to be disconnected from the overall system to be specifically examined on the test bench with a virtual representation of the remaining system. A large part of this required system dynamics depends on the parameters of the PID controller. The adjustment of the parameters is often based on empirical knowledge and empirical methods, which have existed for several decades and consider a highly simplified system dynamics [2]. These procedures are easy to use, but are characterized by a high experimental effort and limited optimization potential. In addition, there are more and more approaches to perform the controller adjustment using metaheuristic algorithms [3, 4]. These work with detailed system models and are able to map the system behaviour in detail. Consequently, they offer a high optimization potential. Since these approaches are based on detailed knowledge about the adjustment procedure and the system to be optimized, they are not yet practicable for test bench operation with varying systems.

Therefore, the authors present in this paper a method that allows to combine the easy handling of the empirical procedures with the optimization potential of metaheuristics. Thus, the user does not need to have a profound knowledge of control engineering and is guided through the adjustment process in the form of targeted support. For this purpose, the construction of system models by using a library based approach and the possibility of automatic system identification as well as a new optimization function are presented. For these only characteristic values such as overshoot, settling time or rise time have to be selected and a qualitative decision has to be made. This enables a simple and robust adjustment of the controllers.

Literature
[1] Tiwari, P.: Optimization of PID Parameter In Control System Tuning With Multi-Objective Genetic Algorithm 4 (2014) 5, S. 7
[2] Ziegler, J. G. u. Nichols, N. B.: Optimum Settings for Automatic Controllers. Journal of Dynamic Systems, Measurement, and Control 115 (1993) 2B, S. 220
[3] Zhang, J., Zhuang, J., Du, H. u. Wang, S.'a.: Self-organizing genetic algorithm based tuning of PID controllers. Information Sciences 179 (2009) 7, S. 1007-1018
[4] Fan u. Joo (Hrsg.): Design for auto-tuning PID controller based on genetic algorithms. 2009

Mr. Kai Wolter, Karlsruher Institut für Technologie - Institut für Produktentwicklung, GERMANY