无沟铺管机北斗导航控制系统设计与试验

针对因田间土壤质地不均匀或表面高低不平,无沟铺管机出现行驶偏摆而导致管道铺设弯曲的问题,设计了基于载波相位差分技术的北斗定位系统(Real Time Kinematic-BeiDou Navigation Satellite System, RTK-BDS)的导航控制系统。采用多模态控制策略,通过传感器检测无沟铺管机工作时的左、右行走马达速度,车辆实际平均车速,发动机功率和两侧行走泵的压力等状态参数,并输入到后向反馈(BackPropagation,BP)神经网络,预测铺管机当前状态分类,使用选择器选择模态控制参数,采用自适应比例-积分-微分(Proportion-Integral-Differential,PID)控制算法进行导航控制。经过田间试验,获取铺管机的模态控制参数和BP神经网络训练样本。导航控制系统上线试验结果表明,导航控制横向超调量为4.58 cm,最大横向误差在±4 cm范围内,平均横向误差在±1.5 cm范围内,能够满足铺管机直线性作业要求。

Design and test of the BDS navigation system for trenchless pipe laying machines

In order to solve the problem of pipeline bending caused by slippage of trenchless pipe laying machine, a navigation control system of trenchless pipe laying machine is designed based on RTK-BDS. According to the idea of multi-mode control algorithm, the multi-mode adaptive PID control algorithm of trenchless pipe laying machine is proposed. In this paper, through the analysis of the kinematic model and slip yaw model of tracked vehicles, it shows that the turning radius of vehicles will increase when slipping; through the analysis of the deviation model of vehicles, the calculation models of the heading deviation and lateral deviation of vehicles are given; through the analysis of the structure and related structural equations of the hydraulic motor of vehicles, the control transmission of the hydraulic motor of trenchless pipe laying machine is given Function. The navigation control system of trenchless pipe laying machine mainly consists of BP neural network classifier, modal selector, knowledge base, adaptive comparator and walking controller. BP neural network classifier uses sensors to detect the running speed of the left track, the right track, the actual average vehicle speed, the engine power and the oil pump pressure of the hydraulic motors on both sides of the trenchless pipe laying machine, so as to obtain the running state of the relevant modes of the vehicle, and input the sampled data to the computer In BP neural network, according to the model trained by BP neural network, the current state classification of vehicles is predicted. The modal selector can obtain the relevant parameters of the current vehicle state from the knowledge base through the classification results of BP neural network, and send them to the adaptive comparator and walking controller. The knowledge base contains the adaptive functions of the vehicle control system and the control parameters of KP, Ki and KD. The adaptive comparator can calculate the weights of the two errors according to the current heading error and lateral error of the vehicle and the reference line, and compare the two weights. If the heading error weight is larger than the lateral error weight, the heading PID controller is selected to control the vehicle navigation, otherwise the lateral PID controller is selected to control the vehicle navigation. Through the field test, the vehicle modal control parameters and BP neural network training samples are obtained. The test shows that the lateral overshoot is within 4.5 cm, the heading error is reduced to ±3.7° and the lateral error is stable within ± 2.6 cm. The linear control experiment shows that the heading error of the control algorithm is within ±5° and the lateral error is within ±2.6 cm. The course error of 96.2% is within ±3° and the lateral navigation error of 89.6% is within ±1 cm. The engineering application test shows that the heading error can be kept within ±7° and the average heading error is within ±3.5° and the transverse error can be kept within ±4 cm and ±1.5 cm respectively. The control system can meet the construction requirements of trenchless pipe laying machine.