Trajectory tracking and balance stabilization control of autonomous motorcycles

Yi, Jingang; Song, Dezhen; Levandowski, Anthony; Jayasuriya, Suhada

doi:10.1109/robot.2006.1642091

Cited by 40 publications

(6 citation statements)

References 16 publications

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“…Steering angle control method: this method has research articles by Y. Tanaka and T. Murakami [1], research by J. Yi, D. Song, A. Levandowski, and S. Jayasuriya [2 ], research by P. Pongpaew [3],… Gyroscope method: this method has been researched and applied by Lit Motors Inc., research by A. V. Beznos, A. M. Formalsky, E. V. Gurfinkel, D.N. Jicharev, A. V. Lensky, K. V. Savitsky, and L. S. Tchesalin [4], research by T. Bui and M. Parnichkun [5],… Balancing method using flywheel: Murata Boy product was developed by Murata Manufacturing Co., Ltd in 2005 [6], ABRB (Auto -Balanced Robotic Bicycle) model of 4 members Aamer Almujahed, Jason Deweese, Linh Duong and Joel Potter of George Mason University, USA [7].…”

Section: Introductionmentioning

confidence: 99%

Research on SimMechanics application to investigate the engine of a self-balancing two-wheeled vehicle model

Huong Thi Thu Nguyen,

Ly Thi Hoang

2024

Global J. Eng. Technol. Adv.

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Since its inception in 2001, self-balancing two-wheeled vehicles have been a topic that has been widely researched around the world. There are many different methods to maintain the balance of a two-wheeled vehicle. This article presents a study on the application of SimMechanics to investigate the engine of a self-balancing two-wheeled vehicle model. Survey results show that when the engine rotates steadily, it will not be able to create torque to help the vehicle balance. At the same time, the voltage (when the torque is largest) and speed (when the engine rotates stably) of the engine have a linear relationship.

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Section: Introductionmentioning

confidence: 99%

Research on SimMechanics application to investigate the engine of a self-balancing two-wheeled vehicle model

Huong Thi Thu Nguyen,

Ly Thi Hoang

2024

Global J. Eng. Technol. Adv.

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“…To improve the control performance, some studies have investigated the nonlinear control for BR. A sliding mode controller combined with low-pass filtering algorithm was proposed in [22], which is shown to be more robust than linear controllers. In another study, to deal with impulse disturbances and system uncertainties, a fuzzy sliding mode controller was designed, but the determination of fuzzy rules was rather complicated.…”

Section: Introductionmentioning

confidence: 99%

Online Series-Parallel Reinforcement-Learning- Based Balancing Control for Reaction Wheel Bicycle Robots on a Curved Pavement

et al. 2023

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The reaction wheel bicycle robot is a kind of unmanned mobile robot with great potential. However, the control of such bicycle robots on a curved pavement under inaccurate model parameters, model uncertainties and disturbances is challenging due to the lateral instability and udneractuated characteristic. Applying conventional control methods to this problem often results in brittle and inaccurate controllers. In this paper, an online serial-parallel combination reinforcement learning with conventional control methods is designed to achieve the path tracking and banlancing control for a reaction wheel bicycle robot on curved pavements. The parallel part of the controller refers to compensate the equilibrium point and the serial part of the controller refers to adjust the parameters of a sliding mode controller that tracks the target roll equilibrium point. The comparison between the proposed controller and several existing controllers in experimental test built in Matlab Simscape illustrates stronger robustness and better control performances.INDEX TERMS reaction wheel bicycle robot, reinforcement learning, robustness, sliding model control.

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“…In this regard, some scholars studied the nonlinear control for BR. In [13], a sliding mode control and low-pass filtering were used to realize balance control on a flat road on a real motorcycle, which showed better anti-interference abilities than the linear controller. In [14], a fuzzy sliding mode controller was designed to deal with impulse disturbance and system uncertainty, but the determination of fuzzy rules was rather complicated.…”

Section: Introductionmentioning

confidence: 99%

Online Reinforcement-Learning-Based Adaptive Terminal Sliding Mode Control for Disturbed Bicycle Robots on a Curved Pavement

et al. 2022

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The reaction wheel is able to help improve the balancing ability of a bicycle robot on curved pavement. However, preserving good control performances for such a robot that is driving on unstructured surfaces under matched and mismatched disturbances is challenging due to the underactuated characteristic and the nonlinearity of the robot. In this paper, a controller combining proximal policy optimization algorithms with terminal sliding mode controls is developed for controlling the balance of the robot. Online reinforcement-learning-based adaptive terminal sliding mode control is proposed to attenuate the influence of the matched and mismatched disturbance by adjusting parameters of the controller online. Different from several existing adaptive sliding mode approaches that only tune parameters of the reaching controller, the proposed method also considers the online adjustment of the sliding surface to provide adequate robustness against mismatched disturbances. The co-simulation experimental results in MSC Adams illustrate that the proposed controller can achieve better control performances than four existing methods for a reaction wheel bicycle robot moving on curved pavement, which verifies the robustness and applicability of the method.

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Trajectory tracking and balance stabilization control of autonomous motorcycles

Cited by 40 publications

References 16 publications

Research on SimMechanics application to investigate the engine of a self-balancing two-wheeled vehicle model

Research on SimMechanics application to investigate the engine of a self-balancing two-wheeled vehicle model

Online Series-Parallel Reinforcement-Learning- Based Balancing Control for Reaction Wheel Bicycle Robots on a Curved Pavement

Online Reinforcement-Learning-Based Adaptive Terminal Sliding Mode Control for Disturbed Bicycle Robots on a Curved Pavement

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