Authors

Abstract

In order to improve the accuracy of model parameters for the rice transplanter, an online parameter identification algorithm for model of the rice transplanter based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline sate. According to the actual vehicle tests, the IPSO-EKF is used to identify the cornering stiffness of the front and rear tires online, and the output data which the identification results were substituted into the model for calculation compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

Abstract in Chinese

为了提高插秧机模型参数辨识精度，提出了一种基于改进的粒子群算法(IPSO)和扩展卡尔曼滤波算法(EKF)的插秧机模型参数在线辨识方法。建立了插秧机动力学模型，用于确定插秧机模型参数。针对EKF算法中噪声矩阵难以选取而影响最佳滤波效果的问题，该算法在离线状态下应用IPSO算法对EKF算法的噪声矩阵进行优化。根据实车试验，采用IPSO-EKF对前后轮胎的侧偏刚度进行在线辨识，将辨识结果代入模型中计算得到输出数据与测量数据进行对比。试验和仿真结果表明,基于IPSO-EKF算法提高了插秧机模型参数的辨识精度，建立了准确的插秧机模型。