Intelligent Adaptive Reaching Law-Based Arbitrary Fixed-Time SMC for Quadcopter Under Disturbances

Abstract

Abstract

This article proposes an intelligent adaptive arbitrary fixed-time sliding mode control (AFxT-SMC) strategy, integrated with an arbitrary fixed-time disturbance observer (AFx-DO), for precise attitude and altitude tracking of quadcopter UAVs. The primary contribution is achieving arbitrary fixed-time convergence of tracking errors and disturbance estimation, allowing designers to freely prescribe any desired settling time, independent of initial conditions and model parameters. In addition, a novel fixed-time reaching law attenuates chattering by driving the discontinuous control component to zero as the sliding surface is approached, while preserving fast fixed-time convergence through adaptive neural network gain tuning. Its coefficients are dynamically tuned by a neural network using backpropagation to handle time-varying dynamics and enhance adaptability. Finally, the arbitrary fixed-time convergence properties of both the proposed arbitrary sliding surface and the AFx-DO are rigorously established through Lyapunov stability analysis. Simulations under external disturbance conditions show that the proposed method outperforms existing adaptive and observer-based controllers in terms of tracking accuracy, transient response, chattering suppression, and energy efficiency. Quantitative analysis results demonstrate that the proposed methodology significantly enhances tracking precision while concurrently reducing control energy expenditure compared to state-of-the-art approaches.