Advanced Pressure Regulation System for Agricultural Sprayers using Model Predictive Control
Abstract
Model Predictive Control (MPC) is like having a crystal ball for controlling systems. It's a method that allows for optimizing control actions by making predictions about how a system will behave in the future. In this research, an MPCbased intelligent control algorithm was created for variable-rate agricultural sprayer robots in order to regulate the goal pressure. The MPC algorithm was described after the modeling and simulation of the spraying system had been established in a MATLAB/Simulink environment. Using the Simulink Support Package for Arduino Hardware in MATLAB/Simulink, the MPC algorithm was implemented in real-time on an Arduino Mega 2560 controller board to verify the accuracy of the simulation results. In this study, MPC was compared to conventional PID control for regulating system pressure. Furthermore, MPC is a revolutionary approach to nonlinear system control that, in comparison to the results obtained with a PID controller, decreases chemical waste and lessens toxicological and environmental risk by achieving zero steady-state error, low transient response, and reduced peak overshoot. In summary, this research demonstrated that MPC is a powerful approach to nonlinear system control. It allows for predicting future behavior and optimizing control actions in real-time. By using this method to control the spraying of agricultural chemicals, this research was able to reduce the risk to the environment and human health, while increasing efficiency and reducing waste.
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