Real-Time Output Linearization in Adaptive Fuzzy Predictive Control for Variable Rate Control

Precise flow rate control in agricultural sprayers is critical for variable-rate application, but is challenging due to nonlinear dynamics, dead zones, and model variations caused by operational changes and nozzle switching. This work aims to develop an efficient control strategy capable of cop these challenges while ensuring compliance with operational constraints.  To this end, a Generalized Predictive Controller (GPC) integrated with a dynamic linearization system and fuzzy logic is designed. A simplified Kalman filter is implemented for real-time estimation of fluidic resistance, enabling adaptive control under varying conditions.  Experimental validation was conducted using the agricultural sprayer development system demonstrating superior performance compared to traditional methods, with improved accuracy and adaptability in the face of disturbances. Thus, the proposed output Linearizing Fuzzy GPC controller effectively handles nonlinearities and operational constraints, offering a practical solution for advanced agricultural spraying systems.