In this paper, stability of a two-wheeled inverted pendulum vehicle was evaluated using rider–vehicle modeling, electromyography of leg’s muscle of rider, and subjective evaluation. A rider and the vehicle are synthetically modeled as a series type double inverted pendulum. Utilizing auto-regressive exogenous (ARX) model methods, correlative relations were found between control gains and stability of the vehicle that achieves better ride comfort. The experimental results show that the higher the control gains, the smaller the activities of leg’s muscles. However, according to subjective evaluation of ride comfort, higher gains did not always achieve better ride comfort. Moreover, the rider-vehicle model has no unstable poles, even when the vehicle model has an unstable pole. The poles of the rider-vehicle model had tendency to have bigger negative real numbers, which suggests stability of the vehicle can be evaluated by the positions of the poles of the rider-vehicle model. Through identification of the rider-vehicle model, the control gains of the rider’s posture were calculated. It was found that the control gain against the rider’s posture is dominant, and the higher the control gains of vehicle, the smaller the gain against the rider’s posture. The results show stability of the vehicle can be evaluated by the control gains against the rider’s posture.
- Dynamic Systems and Control Division
Evaluation of Stability of a Two-Wheeled Inverted Pendulum Vehicle Using Rider-Vehicle Modeling
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Nakano, K, Nakamori, D, Ohori, M, Zheng, R, & Suda, Y. "Evaluation of Stability of a Two-Wheeled Inverted Pendulum Vehicle Using Rider-Vehicle Modeling." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 803-807. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8623
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