Fuzzy Based Control Strategy and Dynamic Torque Adjustment in a Four Wheeled Coconut Tree Climber

ABSTRACT Inherent dangers and limitations of manual labor led to the development of coconut tree climbing robots as a safer and more efficient coconut farming method. Coconut tree climbing robots face significant challenges due to structural constraints, control complexities, and the unique dimensions of coconut trees. Limited research exists on effective designs, with many struggling to balance accuracy, speed, and adaptability. The study presents a novel control law tailored to critical climbing scenarios, enhancing both stability and efficiency. The proposed method ensures reliable ascent and descent by dynamically adapting to challenges such as tree inclines and varying diameters, switching control logic based on situational demands. Key climbing scenarios, including normal cases, wheel‐out‐of‐contact situations, and wheel‐at‐wedge cases, were considered. A fuzzy inference system was integrated to further refine the control strategy, dynamically adjusting torque based on real‐time parameters like inclination, current, encoder values, and diameter variations, thereby optimizing performance across diverse conditions. Detailed static and dynamic analyses shaped the development of a four‐wheeled climber, equipped with gas springs to maximize traction. Simulations validated the proposed control law, achieving a steady state climbing velocity of 0.2 m/s and a displacement of 4 m. Observed transient oscillations and initial peak angular velocities, ranging from to 0.5 rad/s, underscore the importance of accounting for real‐world dynamics. The climber was tested in three different scenarios and achieved a 96.67% overall success rate, completing 29 out of 30 trials. It performed flawlessly in two conditions and had a single failure on a straight tree with varying diameter.