农业轮式移动机器人自适应滑模路径跟踪控制

针对不确定性干扰影响下的农业轮式移动机器人鲁棒自适应路径跟踪问题，提出一种非时间参考的自适应滑模路径跟踪控制方法。首先，选择合适的非时间参考量，建立面向移动机器人路径跟踪控制器设计的相对运动学模型，摆脱了时间和速度因素的影响。其次，对传统的指数趋近律进行改进，设计了新型的快速趋近律。此外，引入RBF神经网络对不确定性干扰进行实时估计，进而提出一种基于新型趋近律的自适应滑模控制方法，并对其性能进行了理论分析与证明。该控制方法既消除了抖振现象又提高了轮式移动机器人在不确定性干扰作用下的路径跟踪性能。最后，通过仿真验证了提出方法的有效性和优越性。

Adaptive Sliding Mode Path Tracking Control of Agricultural Wheeled Mobile Robots

An adaptive sliding mode control method was proposed based on non-time reference, aiming at the path tracking problems of agricultural wheeled mobile robots with uncertain disturbances. First of all, an appropriate non-time reference was selected and then a relative kinematics model was established to cater to designing path tracking controllers of mobile robots. The model got rid of the influences of time and speed. Secondly, the classic exponential reaching law was modified and a new fast reaching law was constructed. Subsequently, the RBF neural network was introduced to estimate the uncertain disturbances, and then an adaptive sliding mode control method was proposed based on the new reaching law. The performance of the control method was demonstrated by theoretical analyses and proofs. By utilizing the proposed method, path tracking performance is improved and chattering phenomena is eliminated when the wheeled mobile robots suffer from the uncertain disturbances. Finally, the effectiveness and superiority was verified by a series of simulation experiments.