RBF神经网络增量式PID自动转向控制系统设计

平地机在田间作业环境下存在复杂非线性时变系统,很难建立精确模型,而传统的PID控制仅仅局限应用于线性系统,控制效果不佳等问题。为了提高田间作业时的转向控制精度,提出了一种基于RBF神经网络增量式PID的控制方法。该方法采用RBF神经网络对增量式PID增益参数进行自适应调整和辨识,并针对控制模型通过仿真实验对比分析了所提出的RBF神经网络增量式PID控制方案与传统PID在平地机转向控制中对方波轨迹跟踪的效果,从而验证了所提出的RBF神经网络增量式PID控制方案的优越性。结果表明:该控制方法对复杂非线性的平地机转向控制系统具有良好的适应性、鲁棒性和实时性,取得了令人满意的控制效果,为后续农业机械自动导航转向控制实际应用环境控制策略的制定提供了有价值的参考。

Design of Automatic Steering Control System Based on RBF Neural Network Incremental PID

Due to the complex nonlinear time-varying system in the field operation environment of the grader,it is difficult to establish an accurate model.However,the traditional PID control methods are only limited to the linear system,and the control effect is not good.In order to improve the accuracy of steering control in field work,an incremental PID control method based on RBF neural network is proposed.The method by using RBF neural network for the incremental PID gain parameters adaptive adjusting and identification,and the control model by simulation experiment,the contrast analysis of the proposed RBF neural network incremental PID control scheme with the traditional PID in grader steering control wave trajectory tracking,which verify the effectiveness of the proposed RBF neural network superiority of incremental PID control scheme.The results show that the control method has good adaptability,robustness and real-time performance for the complicated nonlinear steering control system of grader,and has achieved satisfactory control effect,which provides valuable reference for the formulation of environmental control strategy for the practical application of agricultural machinery automatic navigation steering control.