Research on Stability Control System of Two-Wheel Heavy-Load Self-Balancing Vehicles in Complex Terrain
Source abstract
Abstract
In complex terrain, such as uneven roads or irregular terrain, two-wheeled heavy-duty self-balancing vehicles are easily affected by external interference factors, causing rollover or rendering the vehicle unable to move, which poses a greater challenge to its stability control. Therefore, it is necessary to establish a kinematic model of a two-wheeled vehicle and design a control system to study its driving stability. This paper aims to study the stability control system of a two-wheeled self-balancing vehicle under complex terrain. First, a self-balancing vehicle modeling method based on complex terrain is designed. By analyzing the motion characteristics of the self-balancing vehicle, a kinematic model suitable for complex terrain is established, which provides a basis for subsequent control algorithms. Secondly, a precise control system is designed for different terrain conditions, and parameters such as vehicle attitude, speed and acceleration are adjusted through the Proportional–Integral–Derivative (PID) control algorithm to achieve the smooth operation of the self-balancing vehicle in complex terrain. In addition, a vehicle-mounted camera is used to capture terrain images in real time, and different terrains are accurately identified through the terrain recognition algorithm based on deep learning, thereby determining the friction coefficient and effectively improving the stability of the self-balancing vehicle on complex terrain. The experimental results show that the designed control system can enable the self-balancing two-wheeled vehicle to achieve stable balance control in different terrains, and has good applicability and stability.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Technology and Control Systems · Soil Mechanics and Vehicle Dynamics · Simulation and Modeling Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 510006, China
Guangzhou Automobile Group Co., Ltd., Automotive Engineering Research Institute, Guangzhou 511434, China
Funding
Guangdong Basic and Applied Basic Research Foundation
References (38)
Show source-provided references
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