Realtime Brain-Inspired Adaptive Learning Control for Nonlinear Systems with Configuration Uncertainties

This paper investigates the problem of adap- tive tracking control for quadcopter in the presence of nonlinear configuration uncertainties. It utilizes a real-time brain-inspired learning control (RBiLC) method to address the challenges posed by nonlinear time-varying uncertain instructions. To address the issue of flight control law reconfiguration caused by unknown changes in the fuse- lage configuration (e.g., propellers or motors), this paper introduces an online learning-evaluation-optimization re- construction mechanism based on RBiLC. The proposed adaptive learning controller mitigates the need for exten- sive human resources and reduces the time required for flight controller design. The Lyapunov-Krasovskii function is introduced as a compensatory measure to address the impact of parameter uncertainty on system stability. Fur- thermore, this paper proposes a signed sinusoidal function perturbation estimate to guide the direction and magni- tude throughout the online learning process. The approach conducts a theoretical stability analysis on a quadcopter vehicle considering uncertainties in UAV dynamics mod- eling. The results demonstrate that the proposed scheme achieves superior control and faster adaptation, enabling the system to ultimately converge to a compact set within a limited time domain. Finally, software-in-the-loop (SITL) simulations and flight verification results are presented to validate the proposed control strategy.