Spider monkey based metaheuristic tuning of PID controllers for stability landing of UAV'S with SMP-Auxetic landing gears

The proposed work deals with the study of automatic tuning of PID controllers for perched landing of UAV'S with shape memory This modeling considerably enhanced the range of feasible structures for perch and rest compared with avian-inspired influencers. Though not nature-inspired, and far easier than a foot from a bird, stiff fingers and contact modules were easier to create than avian-inspired gripers with several joint joints per finger and stronger and more durable. Start and landing in flight phases are critical phases.polymer based auxetic landing gears. A metaheuristic tuning is implemented through spider monkey approach for PID controllers in drone perching mechanism. Trials were conducted with open loop for drone perching conditions in measuring the error rates pitch, yaw and roll moment. Fitness function is calculated through regression analysis for the observed experiments. Spider monkey based optimization algorithm is implemented for the fitness function to find the optimal data of Kp, Ki and Kd for minimal error rate of pitch, yaw and roll moment to balance the drone at various perching angles. The provided results have been compared with model predictive controller (MPC) and Generic model control (GMC). It has been noted that SM based PID controller reduces the maximum error rates with 34.6% when compared with MPC and 24.8% when compared with GMC.