An Efficient Neuroadaptive Tracking Control of Uncertain Nonlinear Systems With Full‐State Constraints

This paper proposes a computationally efficient adaptive neural backstepping control (BSC) scheme for nonlinear uncertain systems subject to full‐state constraints and composite disturbances. Barrier Lyapunov functions (BLFs) are properly selected to strictly confine all states within predefined bounds, and radial basis function neural networks (RBF‐NNs) are introduced to accurately approximate system unknown nonlinearities. A streamlined design structure is constructed to reduce computational burden, and a compensated error mechanism is embedded at each BSC step to suppress neural approximation errors and external disturbances, greatly improving system robustness. Rigorous stability analysis proves that all tracking errors converge to an arbitrarily small neighborhood of zero, and all closed‐loop signals achieve semiglobal uniform ultimate boundedness with proper parameter settings. Comparative simulation results and theoretical verification demonstrate the superior performance and inherent advantages of the proposed strategy over traditional control methods.