Roll Movement Control of a Spray Boom Structure using Active Force Control with Artificial Neural Network Strategy

Currently, most of modern sprayers are equipped with suspensions for improving the uniformity of spray application in the field. Therefore, this paper represents the possibility of applying active force control (AFC) technique for the control of a spray boom structure undesired roll movement through a simulation analysis. The dynamic model of the spray boom was firstly defined and an AFC-based scheme controller was designed and simulated in MATLAB environment. Artificial neural network (ANN) is incorporated into the AFC scheme to tune the proportional-derivative (PD) controller gains and compute the spray boom estimated mass moment of inertia. The training of both ANN with multi layer feed forward structure was done using Levenberg-Marquardt (LM) learning algorithm. To evaluate the AFC-ANN control system robustness, various types of disturbances and farmland terrain profileshave been used to excite the spray boom. The results of the study demonstrated that the AFC-based method offers a simple and effective computation compared to the conventional proportional-integral-derivative (PID) control technique in attenuating the unwanted spray boom roll oscillation or vibration. The AFC-ANN scheme is found to exhibit superior performance for different proposed terrain profilesin comparison to the AFC-PD and pure PD counterparts.