Model Predictive Controller Design for a Pesticide Spraying Robot

An essential component of human survival is agriculture.Global human welfare is guaranteed by accurate and effective agricultural control.Conventional agricultural production regulation techniques are time-consuming, labor-intensive, and challenging, while pressing agricultural concerns continue to exist.Agricultural systems are complicated, multivariate, and unpredictable which can be difficult to control using classical control technologies.Model predictive control (MPC) techniques enhance spinning efficiency in a constrained temporal domain, which increases precision, and can provide very accurate control actions with moderate complexity.This paper presents a differential drive robot trajectory tracking approach to achieve minimum error using a mathematical model that controls kinematics and dynamics without coordinate transformation.By taking into account the physics of the engine and frame, a linear state-space dynamic model is developed.The dynamic and kinematic models are enhanced to yield one single state-space linear equation.Constraints on manipulated and controlled variables of the drive motors supply voltage are considered in the control design.To show the performance of the controller, different kinds of trajectories were implemented including circular and eight shaped using MATLAB Simulink software.The findings are examined critically and analytically.Furthermore, investigation was conducted to assess the controller's efficiency both in the presence and absence of unforeseen external disruption along with interior parameter change using a number of key performance measures, which include rise time, settling time, integral time absolute error (ITAE), integral time square error (ITSE), and integral absolute error (IAE).This analysis shows that the suggested MPC regulator is more predictable and flexible to changes in internal parameters and outside influences for the investigated system.