Design and Implementation of Model Predictive Control (MPC) Based Pressure Regulation System for a Precision Agricultural Sprayer

Model Predictive Control (MPC) is a control scheme that involves predicting the future behavior of a system and optimizing control actions to accomplish the desired objective. In this study, we develop an intelligent control algorithm, based on MPC to regulate the target pressure in variable-rate agriculture sprayer robots. Modeling and simulation steps of the spraying system are developed using MATLAB/Simulink environment, before passing to the description of the MPC algorithm. Real-time implementation of the MPC algorithm was conducted on an Arduino Mega 2560 controller board by using Simulink Support Package for Arduino Hardware in MATLAB/Simulink to experimentally validate the preliminary results of simulations. The work evaluates MPC to regulate the pressure in the system and compares the results with a traditional PID control system. Moreover, MPC is a novel method for nonlinear system control that achieves zero steady-state error, low transient response, and reduces peak overshoot compared to the results obtained with a PID controller, thereby reducing the waste of chemicals, and minimizing the toxicology and environmental risk.