Simultaneous Tracking and Balancing Control of Two-Wheeled Inverted Pendulum with Roll-joint using Dynamic Variance MPPI

Abstract

The Two-Wheeled Inverted Pendulum with Rolljoint (TWIP-R) model and Dynamic Variance Model Predictive Path Integral (DV-MPPI) controller are proposed to simultaneously solve tracking and balancing problems. The TWIP-R model’s additional roll joint allows it to better handle centrifugal forces, offering superior performance in high-speed curved driving compared to the traditional TWIP model. Similar to the TWIP model, the TWIP-R model also cannot achieve position tracking without additional kinematic control due to the linearization process. Therefore, controlling the TWIP-R can be achieved using Model Predictive Path Integral (MPPI), which is capable of handling nonlinear control. However, MPPI often suffers from chattering issues due to the use of Gaussian random noise, leading to control instability. DV-MPPI controller dynamically adjusts random noise variance based on realtime state errors, reducing chattering and improving stability. By controlling the TWIP-R using DV-MPPI, we simultaneously solved the tracking and balancing problems without any additional kinematic tracking control, achieving smooth output. Experimental results show our approach is effective, demonstrating the TWIP-R model’s superior performance in balancing and tracking and the benefits of the DV-MPPI.