自适应神经模糊推理系统
计算机科学
控制器(灌溉)
卡西姆
控制理论(社会学)
控制工程
模糊控制系统
人工智能
模糊逻辑
控制(管理)
工程类
农学
生物
作者
Na Dong,Zhiqiang Wu,Zhongke Gao
标识
DOI:10.1177/01423312231183028
摘要
Using electroencephalography (EEG) signals to drive a vehicle could help disabled people expand their range of motion and improve their independence. A brain-controlled vehicle (BCV) is a vehicle that is commanded by analyzing EEG signals. However, the analysis and transmission effect of EEG signals is not ideal, the driving performance of the BCV solely relying on EEG signals is relatively poor. In this paper, to solve this problem, we propose a dynamic shared control method based on adaptive network-based fuzzy inference system (ANFIS). First, an ANFIS intelligent controller is designed to automatically make decisions according to the state of the vehicle. Then, safety coefficient and intention coefficient are proposed to evaluate the safety and driving intention of the brain-controlled driver. Finally, a fuzzy controller with safety and intention coefficients as inputs and brain-controlled driver weights as outputs is designed. The controller is the embodiment of a human–machine interaction, which allows the driver to maintain maximum control authority over the BCV under safe conditions by dynamically balancing the control authority of the brain-controlled driver and the ANFIS controller on the BCV. To verify the effectiveness of the proposed method, a joint simulation platform of Carsim and Matlab is established, and several groups of comparative simulation experiments are carried out, through which, it is demonstrated that the proposed method can effectively avoid road deviation while well maintaining the control authority of the brain-controlled driver.
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