线控转向电动叉车操纵稳定性的研究

发布时间：2018-02-26 21:51

本文关键词： 叉车线控转向系统模糊神经网络神经网络PID 主动转向　出处：《合肥工业大学》2017年硕士论文　论文类型：学位论文

【摘要】：电动叉车在狭小空间内需要频繁转向,负载变化明显,对于转向系统的要求高于一般车辆。线控转向系统作为最新一代的转向系统,具有传动比可自由设计的优点,能够很好的满足叉车的转向要求。线控转向系统最大的特点就是传动比可自由设计,在研究横摆角速度增益恒定和侧向加速度增益恒定的变传动比控制的基础上,设计了基于这两种增益联合恒定的变传动比控制方案。考虑到叉车的动态响应,设计了基于模糊神经网络的变传动比控制方案。结合TE60电动叉车,进行仿真试验,证明了两种增益联合恒定的变传动比控制对于叉车的转向性能有很好的提高。与传统的转向系统相比,基于模糊神经网络的变传动比控制的动态性能更好。对转向执行机构进行PID控制,同时针对PID参数整定的问题设计了基于matlab非线性优化(NCD)工具箱优化PID参数的方法。仿真结果表明采用非线性优化PID参数的方法便捷可靠。针对轮胎的非线性特征,建立了电动叉车实际的非线性车辆模型。在实际应用中,质心侧偏角难以直接测量到,因此本文设计了采用横摆角速度和侧向加速度来估计质心侧偏角的状态观测器的方法。本文针对实际叉车的动态响应与理想模型参考值的偏差,设计了横摆角速度和质心侧偏角联合反馈神经网络PID的主动转向控制方法,仿真结果表明,采用主动转向的方法,使得叉车低速转向灵敏,高速转向稳健,提高了叉车的操纵稳定性和鲁棒性。
[Abstract]:The electric forklift truck needs frequent steering in a narrow space, the load changes obviously, and the requirement for steering system is higher than that for ordinary vehicles. As the latest generation of steering system, the wire steering system has the advantage of freely designing the transmission ratio. The main characteristic of the wire steering system is that the drive ratio can be designed freely. On the basis of studying the variable drive ratio control of constant yaw angular velocity gain and constant lateral acceleration gain, The variable transmission ratio control scheme based on these two kinds of gain is designed. Considering the dynamic response of the forklift truck, the variable transmission ratio control scheme based on fuzzy neural network is designed. Combined with the TE60 electric forklift, the simulation experiment is carried out. It is proved that the steering performance of forklift truck can be improved by two kinds of variable transmission ratio control with constant gain, and compared with the traditional steering system, The variable transmission based on fuzzy neural network has better dynamic performance than the control. The steering actuator is controlled by PID. At the same time, the method of optimizing PID parameters based on matlab nonlinear optimization toolbox is designed for the problem of PID parameter tuning. The simulation results show that the method of nonlinear optimization of PID parameters is convenient and reliable. The actual nonlinear vehicle model of electric forklift is established. In practical application, it is difficult to measure the side deflection angle of mass center directly. Therefore, this paper designs a method to estimate the side deflection angle of mass center by using yaw velocity and lateral acceleration. The deviation between the dynamic response of the actual forklift and the reference value of the ideal model is discussed in this paper. An active steering control method based on the combination of yaw angular velocity and sideslip angle of mass center with feedback neural network (PID) is designed. The simulation results show that the method of active steering makes forklift turning sensitive at low speed and robust at high speed. The handling stability and robustness of forklift truck are improved.
【学位授予单位】：合肥工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TH242

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