Hybrid adaptive PID control strategy for UAVs using combined neural networks and fuzzy logic.

This paper presents a novel hybrid combined neural network and fuzzy logic adaptive proportional, integral, and derivative(NNPID+FPID) control strategy that integrates neural networks and fuzzy logic for optimizing Unmanned Aerial Vehicle(UAV) dynamics by tuning the gains of a PID controller. The proposed approach leverages the strengths of each technique by applying neural networks to fine-tune the y and ψ states, while fuzzy logic enhances the performance of x, z, ϕ, and θ dynamics. A single-layer neural network with 10 hidden neurons is utilized to adjust PID gains for the y and ψ states using proportional, integral, and derivative errors ([Formula: see text]) as inputs. The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. Concurrently, fuzzy logic employs heuristic rules to dynamically tune PID gains for the remaining states, based on input errors and their derivatives. Membership functions map inputs to gains to ensure real-time adaptability. The hybrid method outperforms standalone neural network(NNPID) and fuzzy logic(FPID) approaches by significantly improving trajectory tracking performance and overall UAV control efficiency. This work demonstrates the effectiveness of combining neural networks and fuzzy logic to address the multi-dimensional control challenges of UAV systems.