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Research on Dynamic Variable Transmission Ratio and Handling Stability for Steer by Wire Vehicle Based on Neural Network

机译:基于神经网络的线缆转向动态可变传动比和操纵稳定性研究

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The handling stability and control requirements of electric forklift are analyzed. Forklift model with linear two degree of freedom (2DOF) is presented, which provides a verification model for variable transmission ratio (VTR) design. The concept of the ideal transmission ratio is described, the VTR control method based on yaw rate gain constant and lateral acceleration gain constant is studied, a new VTR control method based on two kinds of gain constant combination is put forward, and the simulation comparison is made for three kinds of static VTR control methods. A dynamic VTR control method based on fuzzy neural network (FNN) is designed for complex working conditions, simulation results show that the VTR based on FNN has strong resistance to the disturbance of its own parameters. It is only related to the velocity and the hand-wheel angle, and adapts to the complex dynamic conditions, helps to improve the handling stability of forklift. The research shows that the three VTR control methods with fixed gain are derived through mathematical model, which belongs to static control, and the dynamic VTR control method based on FNN has better dynamic adaptability.
机译:分析了电叉车的处理稳定性和控制要求。提出了具有线性两度自由(2DOF)的叉车模型,其为可变传输比(VTR)设计提供了验证模型。描述了理想传输比的概念,研究了基于横摆率增益恒定和横向加速度增益常数的VTR控制方法,提出了一种基于两种增益恒定组合的新VTR控制方法,并进行仿真比较采用三种静态VTR控制方法制作。基于模糊神经网络(FNN)的动态VTR控制方法专为复杂的工作条件而设计,仿真结果表明,基于FNN的VTR对其自身参数的干扰具有很强的抵抗力。它仅与速度和手工轮角度有关,并适应复杂的动态条件,有助于提高叉车的处理稳定性。该研究表明,通过数学模型导出具有固定增益的三个VTR控制方法,属于静态控制,基于FNN的动态VTR控制方法具有更好的动态适应性。

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