Planning the Yaw Correction Trajectory Based on Current Motor Detection

Author(s): Weiqiang Ying, Cheng Kuang, Lingyan Zhang, Cheng Yao, Fangtian Ying and Shijian Luo

This paper describes distributed adaptive methods for solving the trajectory distortion control problem of intelligent lawnmowers. The strategy we have chosen is to use the association of motor current changes with changes in the movement patterns of the trajectory to try to modify the relationship between the fuzzy controller and the Proportional Integral Derivative (PID) controller based on the traditional fuzzy PID control theory to design a new low-cost control scheme. The scheme uses the differences between the current detection and the linearised models as data feedback and interpolates these linear models with the Takagi-Sugeno (T-S) fuzzy method to approximate the entire non- linear model. Then, the concept of parallel distributed compensation (PDC) synthesizes a state feedback controller. A Linear Quadratic Regulator (LQR) is used to stabilize the system and achieve the desired response. A PID fuzzy control method is then used to form the control of the intelligent lawnmower motor. This strategy uses data feedback obtained by monitoring the left and the right motor current fluctuations, and it can be seen from the practical results that the proposed method is effective in obtaining an ideal linear path with good tracking behaviour in various situations. This paper uses the current monitoring change control method to determine whether the bias voltage is tested and confirms that the method can achieve stable path control.