Dynamic-gain neural network observer based prescribed performance backstepping sliding mode control of uncertain nonlinear systems

This work presents a prescribed performance backstepping sliding mode control framework designed for a class of nonlinear systems with unknown disturbances. A key feature of the proposed method is the incorporation of a dynamic-gain neural network observer to handle system uncertainties and estimate unmeasurable states. In contrast with static gain observers, it adaptively adjusts its gain in real time, eliminating precise tuning. Moreover, an innovative integral nonsingular fast terminal sliding mode control (INFTSMC) strategy integrated with prescribed performance control (PPC) is developed to ensure that tracking errors adhere to pre-specified transient and steady-state requirements, enhancing reliability in practical applications. The control method manages dynamics, while the neural network observer compensates for nonlinearities, ensuring robustness under uncertainty. The system stability is analyzed via the Lyapunov theory. Simulation results demonstrate the effectiveness of the method.