Model Analysis of Unmanned Bicycle and ADRC Control

Zhang, Yongli; Liu, Yu; Yi, Guorong

doi:10.1109/iciba50161.2020.9277227

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Therefore, it is of interest to add two-wheeled vehicles the safety features implemented in cars, such as anti-lock braking, traction control, or stability systems, given that such systems resulted in an excellent accident reduction for cars [33][34][35][36]. Thus, adequate systems could help the rider stay in control of the two-wheeled vehicle during dangerous situations and avoid accidents [37,38]. However, the same results do not occur when applying such systems for two-wheeled vehicles [39,40].…”

Section: Formulation Of the Problem Of Interest For This Investigationmentioning

confidence: 99%

A Multibody System Approach for the Systematic Development of a Closed-Chain Kinematic Model for Two-Wheeled Vehicles

2021

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In this investigation, a closed-chain kinematic model for two-wheeled vehicles is devised. The kinematic model developed in this work is general and, therefore, it is suitable for describing the complex geometry of the motion of both bicycles and motorcycles. Since the proposed kinematic model is systematically developed in the paper by employing a sound multibody system approach, which is grounded on the use of a straightforward closed-chain kinematic description, it allows for readily evaluating the effectiveness of two alternative methods to formulate the wheel-road contact constraints. The methods employed for this purpose are a technique based on the geometry of the vector cross-product and a strategy based on a simple surface parameterization of the front wheel. To this end, considering a kinematically driven vehicle system, a comparative analysis is performed to analyze the geometry of the contact between the front wheel of the vehicle and the ground, which represents a fundamental problem in the study of the motion of two-wheeled vehicles in general. Subsequently, an exhaustive and extensive numerical analysis, based on the systematic multibody approach mentioned before, is carried out in this work to study the system kinematics in detail. Furthermore, the orientation of the front assembly, which includes the frontal fork, the handlebars, and the front wheel in a seamless subsystem, is implicitly formulated through the definition of three successive rotations, and this approach is used to propose an explicit formulation of its inherent set of Euler angles. In general, the numerical results developed in the present work compare favorably with those found in the literature about vehicle kinematics and contact geometry.

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Section: Formulation Of the Problem Of Interest For This Investigationmentioning

confidence: 99%

A Multibody System Approach for the Systematic Development of a Closed-Chain Kinematic Model for Two-Wheeled Vehicles

2021

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“…During the bicycle's forward movement, it may encounter environmental factors (e.g., uneven road surfaces and wind instability interference). Moreover, the system is a typical nonlinear and underactuated control system (Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Unmanned Bicycle Balance Control Based on Tunicate Swarm Algorithm Optimized BP Neural Network PID

Zhang

et al. 2023

International Journal of Information Technologies and Systems Approach

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In this study, the authors introduce a novel approach that leverages the tunicate swarm algorithm (TSA) to optimize proportional-integral-derivative (PID) controller based on a back propagation (BP) neural network. The core objective of the approach is to manage and counteract uncertainties and disturbance that may jeopardize the balance and stability of self-driving bicycles in operation. By using the self-learning capabilities of BP neural networks, the controller can dynamically adjust PID parameters in real time. This enables an enhanced robustness and reliability during operation. Further bolstering the efficiency of our controller, the authors use the TSA to optimize the initial weights of a neural network. This effectively mitigates the commonly associated with slow convergence and being entrapped in local minima. Through simulation and experimentation, the findings reveal that the TSA-optimized BP neural network PID controller dramatically improves dynamic performance and robustness. It also proficiently manages changes in the environment such as wind and ground bumps. Therefore, the proposed controller design offers an effective solution to the balancing problem of self-driving bicycles and paves the way for a promising future in designing versatile controllers with broad application potential.

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Self-balance Control of Bicycle with Inertial Wheel Pendulum based on Linear ADRC

Liu

Jin

Huo

et al. 2022

2022 China Automation Congress (CAC)

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Model Analysis of Unmanned Bicycle and ADRC Control

Cited by 5 publications

References 8 publications

A Multibody System Approach for the Systematic Development of a Closed-Chain Kinematic Model for Two-Wheeled Vehicles

A Multibody System Approach for the Systematic Development of a Closed-Chain Kinematic Model for Two-Wheeled Vehicles

Unmanned Bicycle Balance Control Based on Tunicate Swarm Algorithm Optimized BP Neural Network PID

Self-balance Control of Bicycle with Inertial Wheel Pendulum based on Linear ADRC

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