MPPI Control of a Self-Balancing Vehicle Employing Subordinated Control Loops

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 performance is limited in full-scale applications. In most applications, 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. Subordinate 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. Videos of the self-balancing vehicle are available at: https://tinyurl.com/mvn8j7vf