拖拉机自动转向系统容错自适应滑模控制方法

为提高拖拉机自动转向系统的可靠性,该文提出了一种具有前轮转角容错检测能力的径向基函数(radial basis function,RBF)网络自适应滑模控制方法。综合考虑拖拉机姿态信息和控制输出,基于卡尔曼滤波算法推导得出拖拉机前轮转角的两个估计值,并结合角度编码器实际测量值设计了前轮转角容错检测输出算法;以容错输出算法的输出值作为状态量,提出一种利用RBF网络进行干扰补偿的前轮角度自适应滑模控制方法,并通过仿真试验验证了算法的有效性。开展了拖拉机前轮转角容错检测和自动控制试验,结果显示:基于侧向加速度的转角预估值最大误差为2.94?,均方根误差为0.81?;基于横摆角速度的转角预估值的最大误差为1.73?,均方根误差为0.12?;当人为施加故障干扰时,算法可以提供容错的转角输出;拖拉机转向控制系统可以快速跟踪期望前轮角度且超调量较小,最大控制误差为0.21?,均方根误差为0.07?。试验结果表明,容错自适应滑模控制方法提高了自动转向控制系统的可靠性和准确性,有助于解决拖拉机前轮转角测量装置故障率高的问题。

Fault-tolerant adaptive sliding mode control method of tractor automatic steering system

Abstract: Unmanned tractors have become the focus of research in the field of intelligent agricultural machinery in recent years, which can effectively improve the agricultural productivity and the accuracy of operations. Automatic steering control system was a prerequisite for driverless tractors, and the key technologies included steering wheel angle detection method and angle tracking control algorithm. Steering wheel angle measurement results were the direct factors affecting the navigation effect. Encoder measurement and angular rate measurement were 2 commonly used front wheel angle measurement methods. Absolute angle measurement method had higher detection accuracy, but there were too many mechanical connectors using this method, and the calibration work was complex. The angular rate measurement method generally used inertial devices, which were easy to install and had a long working life. However, there was a random drift in the gyroscope and the accumulated error would affect the measurement accuracy. In practical applications, whatever the above-described measuring methods used, the angle measuring device was the most easily damaged component of the entire control system. For example, the angle measuring mechanism was easily damaged during operation by crops when it was installed in the position of the tractor front axle, it would cause signal output failure, and the reliability and safety of the unmanned system would be affected. In addition, the steering control system would be subject to a variety of non-linear factors such as mechanical clearance and hydraulic system lag during the working process, resulting in poor control effects. It was necessary to compensate for the uncertainties to further enhance the effect of angle tracking control. In order to solve the problems above, an RBF adaptive sliding mode control method with the ability of fault-tolerant detection of front wheel angle was proposed. First of all, a discrete state equation of the control system was deduced according to the structure of the tractor steering control system, and then the relationship between the front wheel angle and inertial information of the tractor''s steering center including the lateral acceleration and the yaw rate was deduced respectively from the linear two-degree-of-freedom vehicle model. Secondary, in order to obtain a high redundancy value of the front wheel angle, 2 estimated values of the front wheel angle were obtained by Kalman filter, and a fault diagnosis algorithm and a fault-tolerant output algorithm were designed by comparing the residual threshold between the angle encoder value and these 2 estimated values. Thirdly, an adaptive sliding mode control method of the front wheel angle was proposed, which used an RBF neural network to identify the uncertain disturbance in order to ensure that the steering control system could accurately and timely track the expected rotation angle under the uncertainty interference factors. Finally, the fault tolerance test and automatic control test of the front wheel angle were carried out. The experimental results showed that the maximum error of the angular estimation based on the lateral acceleration was 2.94° and the root mean square error was 0.81°, while the maximum error of the angular estimation based on the yaw rate was 1.73° and the root mean square error was 0.12°. The fault-tolerant algorithm could automatically switch to the estimation value when the encoder artificially exerted an interference signal, and it could effectively replace the role of encoder and improve the reliability of the tractor automatic driving system. The experimental results of the angle control system showed that the performance indicators of the adaptive sliding mode control algorithm based on RBF network were better than the traditional PID (proportion, integration, differentiation) control method, and it could track the desired angle quickly with small overshoot, and the maximum error of angle control was 0.21° and the root mean square error was 0.07°. Test results showed that the fault-tolerant adaptive sliding mode control method can improve the reliability and accuracy of the automatic steering control system and help to solve the problem of high failure rate of the front wheel rotation angle measuring device of the tractor autopilot system.