Under the complex agricultural operation environment, reliable navigation system is the basic guarantee to realize the agricultural robot automated operation. This study focuses on improving navigation accuracy and control accuracy and conducts related research on autonomous navigation control of agricultural robots. This article discusses the advantages of using strict convergence criteria and combining Sage–Husa adaptive filtering with strong tracking Kalman filtering and then proposes an improved adaptive Kalman filter algorithm. The new algorithm can effectively suppress the filter divergence, improve the dynamic performance of the filter, and ensure its better filtering accuracy and strong adaptive ability to improve navigation accuracy of GPS. Further variable structure switching method is used to prevent proportional integral differential (PID) controller integral saturation phenomenon, which effectively solves the controller over-saturation problem. And combining this method with an improved adaptive filtering algorithm not only can effectively inhibit control interference but also achieve the anti-saturation effect, thereby enhancing the stability and accuracy of the control system. Finally, the simulation and experiment of the new method show that the proposed method greatly improves the ability of the filter to suppress divergence and control precision.

中文翻译：

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基于改进自适应滤波的农业机器人自主导航控制

在复杂的农业作业环境下，可靠的导航系统是实现农业机器人自动化作业的基本保障。本研究以提高导航精度和控制精度为重点，开展农业机器人自主导航控制相关研究。本文讨论了使用严格收敛标准并将Sage-Husa自适应滤波与强跟踪卡尔曼滤波相结合的优点，然后提出了一种改进的自适应卡尔曼滤波算法。新算法能有效抑制滤波器发散，提高滤波器的动态性能，保证其较好的滤波精度和较强的自适应能力，提高GPS导航精度。进一步采用变结构切换方法，防止比例积分微分（PID）控制器积分饱和现象，有效解决控制器过饱和问题。并将该方法与改进的自适应滤波算法相结合，不仅可以有效抑制控制干扰，还可以达到抗饱和效果，从而提高控制系统的稳定性和准确性。最后，新方法的仿真和实验表明，该方法大大提高了滤波器抑制发散的能力和控制精度。并将该方法与改进的自适应滤波算法相结合，不仅可以有效抑制控制干扰，还可以达到抗饱和效果，从而提高控制系统的稳定性和准确性。最后，新方法的仿真和实验表明，该方法大大提高了滤波器抑制发散的能力和控制精度。并将该方法与改进的自适应滤波算法相结合，不仅可以有效抑制控制干扰，还可以达到抗饱和效果，从而提高控制系统的稳定性和准确性。最后，新方法的仿真和实验表明，该方法大大提高了滤波器抑制发散的能力和控制精度。