叉车线控转向主动控制与路感模拟研究

发布时间：2018-05-08 11:59

本文选题：叉车 + 线控转向　；参考：《合肥工业大学》2017年硕士论文

【摘要】：作为专用于货物搬运的车辆,叉车常用于港口、仓库以及车间等有限区域内的短距离物料搬运,工作时具有转弯多、道路窄等特点。因此,转向的稳定性和操纵性是叉车性能的关键因素,越来越受到人们的重视。文章将线控转向技术与叉车相结合,以TK30型电动叉车为研究对象,对线控转向叉车的主动转向控制和路感模拟策略进行了相关研究。首先,在国内外车辆动力学研究的基础上,分析叉车jk摆、侧向以及侧倾三个方向上的运动和受力,结合“魔术公式”轮胎模型和载荷转移建立了非线性三自由度叉车动力学模型。以此模型为基础,忽略轮胎的非线性,采用线性轮胎模型导出了线性三自由度叉车参考模型。前者主要用作仿真试验的试验对象,后者主要用于系统分析和设计。然后,针对车辆质心侧偏角不易测量的问题,基于线性三自由度叉车模型以横摆角速度和侧倾角为观测量,并考虑模型误差的影响设计了自适应卡尔曼滤波器估算叉车质心侧偏角。为了提高估计的精度,给出了自适应卡尔曼滤波器自适应因子的遗传算法在线优化策略。仿真试验证明了该方法的有效性。接着,选取叉车jk摆角速度和质心侧偏角为反馈变量,以线性三自由度参考叉车模型的稳态输出为理想值,基于比例控制设计了线控转向叉车主动转向系统的控制器,该控制器能通过横摆角速度和质心侧偏角的综合反馈对叉车的转向轮转角进行实时修正,从而达到提高叉车操纵稳定性的目的。仿真试验结果表明该策略是有效的。最后,研究了传统机械式转向车辆的路感力矩产生机理,采用动力学计算的方式得出叉车的路感力矩。选取有刷直流电机作为路感电机,针对路感控制系统中的输入干扰、控制干扰以及测量噪声,将卡尔曼滤波器引入到闭环系统中,用于改善路感的舒适度,同时提升系统的可靠性和抗干扰能力。仿真试验结果表明该策略是有效的。
[Abstract]:As a special vehicle for cargo handling, forklifts are often used for short distance material handling in limited areas such as ports, warehouses and workshops. Therefore, steering stability and maneuverability are the key factors of forklift truck performance. In this paper, combining wire steering technology with forklift truck, taking TK30 electric forklift as research object, the active steering control and road sense simulation strategy of wire steering forklift are studied. First of all, based on the research of vehicle dynamics at home and abroad, the movement and force in the three directions of fork lift JK pendulum, roll and roll are analyzed Based on the "magic formula" tire model and load transfer, a nonlinear dynamic model of three degrees of freedom forklift truck is established. Based on this model, the linear three degree of freedom reference model of forklift truck is derived by using the linear tire model, which neglects the nonlinearity of the tire. The former is mainly used as the test object of simulation test, and the latter is mainly used in system analysis and design. Then, aiming at the problem that the side deflection angle of the vehicle's mass center is not easy to be measured, based on the linear three-degree-of-freedom forklift model, the yaw angular velocity and the lateral inclination angle are taken as the observed quantities. Considering the influence of model error, an adaptive Kalman filter is designed to estimate the side deflection angle of forklift mass center. In order to improve the estimation accuracy, a genetic algorithm online optimization strategy of adaptive Kalman filter adaptive factor is presented. Simulation results show that the method is effective. Then, the controller of the forklift steering forklift active steering system is designed based on proportional control by selecting the JK pendulum velocity and the side deflection angle of the center of mass as the feedback variables, and taking the steady output of the linear three degree of freedom reference forklift model as the ideal value. The controller can modify the steering angle of forklift truck in real time through the comprehensive feedback of yaw velocity and side deflection angle of mass center, so as to improve the handling stability of forklift truck. Simulation results show that the strategy is effective. Finally, the mechanism of the road sensormoment of the traditional mechanical steering vehicle is studied, and the road sense torque of the forklift truck is obtained by dynamic calculation. The brushless DC motor is selected as the road induction motor. Aiming at the input interference, control interference and measurement noise in the road sense control system, the Kalman filter is introduced into the closed-loop system to improve the comfort of the road sense. At the same time, improve the reliability of the system and anti-interference ability. Simulation results show that the strategy is effective.
【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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