Optimization and experimental analysis of sweet potato ship-shaped transplanting trajectory using particle swarm algorithm

The mechanization of ship-shaped transplanting is currently an urgent problem that should be solved.The movement trajectory of the transplanting mechanism is the key technology to perform ship-shaped transplanting.In this study,a ship-shaped transplanting trajectory was built and a mathematical model of a four-link transplanting mechanism was developed based on the requirements of the sweet potato transplanting agronomic technology.The particle swarm optimization algorithm was used,with the length of the four-bar mechanism and the installation angle of the fixed bar serving as the variables to optimize.The objective was to minimize the deviation of the ship-shaped transplanting trajectory,yielding an iterative optimization solution.The MATLAB simulation results showed that the penalty factors of different proportions in the adaptive particle swarm optimization algorithm affected the transplanting trajectory.The optimal penalty factor parameters areα=0.6,β=0.4.They ensure that the transplanting trajectory fulfills the agronomic requirements,and limit the deviation in the return trajectory of the mechanism.The sizes of the optimized four-bar mechanism were 110,312,245,160,360,and,160 mm.The determined installation angle was 100°.The results of the field experiments demonstrated that the optimized four-bar transplanting mechanism can better fulfill the agronomic technical requirements of sweet potato ship-shaped transplanting.For a transplanting speed of 0.2 m/s,the average qualified rates of insertion depth,insertion length,and tail height were equal to 94.00%,93.83%,and 91.67%.The results obtained in this study provide a theoretical basis and technical support for studying and developing sweet potato ship-shaped transplanting machinery.

Trajectory tracking control of agricultural vehicles based on disturbance test

To improve the trajectory tracking robust stability of agricultural vehicles,a path tracking control method combined with the characteristics of agricultural vehicles and nonlinear model predictive control was presented.Through the proposed method,the path tracking problem can be divided into two problems with speed and steering angle constraints:the trajectory planning problem,and the trajectory tracking optimization problem.Firstly,the nonlinear kinematics model of the agricultural vehicle was discretized,then the derived model was inferred and regarded as the prediction function plant for the designed controller.Second,the objective function characterizing the tracking performance was put forward based on system variables and control inputs.Therefore,the objective function optimization problem,based on the proposed prediction equation plant,can be regarded as the nonlinear constrained optimization problem.What’s more,to enhance the robust stability of the system,a real-time feedback and rolling adjustment strategy was adopted to achieve optimal control.To validate the theoretical analysis before,the Matlab simulation was performed to investigate the path tracking performance.The simulation results show that the controller can realize effective trajectory tracking and possesses good robust stability.Meanwhile,the corresponding experiments were conducted.When the test vehicle tracked the reference track with a speed of 3 m/s,the maximum lateral deviation was 13.36 cm,and the maximum longitudinal deviation was 34.61 cm.When the added horizontal deviation disturbance Yr was less than 1.5 m,the controller could adjust the vehicle quickly to make the test car return to the reference track and continue to drive.Finally,to better highlight the controller proposed in this paper,a comparison experiment with a linear model predictive controller was performed.Compared to the conventional linear model predictive controller,the horizontal off-track distance reduced by 36.8%and the longitudinal deviation reduced by 32.98%when performing circular path tracking at a speed of 3 m/s.