Work

Refinement of Control Strategies for Wheel-Fan Systems in High-Speed Air-Floating Vehicles Operating in Atmospheric Pressure Pipelines

Year: 2024

Type: article

Abstract: This study explored the optimization of control systems for atmospheric pipeline air-floating vehicles traveling at ground level by introducing a novel composite wheel-fan system that integrates both wheels and fans. To evaluate the control impedance, the system simulates road conditions like inclines, uneven surfaces, and obstacles by using fixed, random, and high torque settings. The hub motor of the wheel fan is managed through three distinct algorithms: PID, fuzzy PID, and the backpropagation neural network (BP). Each algorithm’s control strategy is outlined, and tracking experiments were conducted across straight, circular, and curved trajectories. Analysis of these experiments supports a hybrid control approach: initiating with fuzzy PID, employing the PID algorithm on straight paths, and utilizing the BP neural network for sinusoidal and circular paths. The adaptive capacity of the BP neural network suggests its potential to eventually supplant the PID algorithm in straight path scenarios over extended testing and operation, ensuring improved control performance.

Source: Aerospace

DOI: 10.3390/aerospace11120974

Authors: Kun Zhang, Bin Jiao, Yuliang Bian, Zeming Liu, Tiehua Ma, Changxin Chen

Institution: North University of China

Language: English

Topic: Control and Dynamics of Mobile Robots

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,728