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In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model 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 state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was 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. 摘要 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,将辨识的侧偏刚度值代入模 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。 INTRODUCTION With the intelligent and modernized development of agricultural machinery, a rice transplanter with autonomous navigation has played a key role in rice planting. However, an accurate dynamic model must be established before the model-based rice transplanter control algorithm is designed. The accuracy and real-time of parameters for the dynamic model directly affect the path tracking control of the rice transplanter (Kayacan E., et al., 2019). The cornering stiffness of the front and rear tires for a rice transplanter is an important parameter of the dynamic model, it needs to be accurately identified online. At present, it is difficult for sensors to directly measure the cornering stiffness of tires in real time, so the system identification method is particularly important to identify the parameter of vehicle models. The least square algorithm is widely used in parameter identification. The non-linear least squares algorithm is used to identify the vehicle parameter, namely, the cornering stiffness of the front and rear tires (Kayacan E., et al., 2015). The offline least squares algorithm is used to identify tire cornering stiffness by real vehicle tests (Li L., et al., 2016). They don’t consider that the cornering stiffness is a time-varying parameter. The variable forgetting factor recursive least squares algorithm is used to identify online parameters such as the vehicle mass and the rotational inertia (Khaknejad M B., et al., 2011). Although the algorithm meets the requirements of online identification, it is only suitable for simple linear models and can’t be directly used for nonlinear models. The modern intelligent optimization algorithms are also applied to parameter identification. Vol. 61, No. 2 / 2020 INMATEH – 26 The eight unknown parameters of the semi-trailer train model are identified by genetic algorithm and L-M optimization algorithm (Liang Q.Z., 2016). The improved genetic algorithm is presented to optimize the parameters of the inertia for three coordinate axes (Zhang Q.C.,2007). The genetic algorithm can identify multiple parameters at the same time and can reach the global optimal solution, but the calculation is large and complicated, and the identification of time-varying parameters can’t be well applied. Considering the real-time nature of the algorithm, the EKF algorithm is used to identify the vehicle’s mass and the cornering stiffness of front and rear tires online (Wang J., 2019). The EKF is used to identify the cornering stiffness of the front and rear tires of vehicle (Reina et al., 2019). As the noise matrices are selected according to experience, the filtering didn’t reach the expected effect. A method is presented for identifying parameters of a vehicle dynamic model (Best., et al., 2007). They used an adaptive EKF with variable fading factor to identify vehicle’s mass, the rotational inertia and wheelbase parameters online, but this algorithm was more complicated and adjusted more parameters. Considering the above-mentioned problems, this paper proposes an online parameter identification algorithm based on IPSO-EKF. As the selection of the process noise matrix Q and the measurement noise matrix R determines the convergence for EKF algorithm, and the true value of the Q and R can’t be obtained directly, combined with PSO algorithm, Q and R matrices are identified offline to determine the optimal noise matrices. This algorithm not only solves the difficult problem of determining the noise matrices in traditional EKF, but also doesn’t need to change the original EKF algorithm. Therefore, the IPSO-EKF algorithm is used to identify the cornering stiffness of the front and rear tires for the rice transplanter online. MATERIALS AND METHODS Dynamic model of the rice transplanter According to motion characteristics of rice transplanter, the two degrees of freedom of the bicycle model with only lateral and yaw motion are established. In the range of small angles, the lateral tire forces are assumed to be proportional to the slip angles. The front wheel of the rice transplanter is steered and the front wheel has a small angle. The bicycle model is shown in Fig.1.

ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM