Open Access

Hannes Homburger, Stefan Wirtensohn, Johannes Reuter

• Recently published nonlinear model-based control approaches achieve impressive performances in complex real- world applications. However, due to model-plant mismatches and unforeseen disturbances, the model-based controller’s per- formance is limited in full-scale applications. In most applica- tions, low-level control loops mitigate the model-plant mismatch and the sensitivity to disturbances. But what is the influence of these low-level control loops? In this paper, we present the model predictive path integral (MPPI) control of a self- balancing vehicle and investigate the influence of subordinate control loops on closed-loop performance. Therefore, simulation and full-scale experiments are performed and analyzed. Subor- dinate control loops empower the MPPI controller because they dampen the influence of disturbances, and thus improve the model’s accuracy. This is the basis for the successful application of model-based control approaches in real-world systems. All in all, a model is used to design a low-level controller, then its closed-loop behavior is determined, and this model is used within the superimposed MPPI control loop – modeling for control and vice versa.