Disturbance Observer‐Based Adaptive Finite Time Tracking Control for Fractional Order Systems With Full State Constraints and Input Saturation

ABSTRACT This article focuses on the issue of adaptive neural network finite‐time control scheme for a class of full state constrainted fractional‐order uncertain nonlinear systems with external disturbances and input saturation. To overcome the parameter explosion problem, a new finite‐time fractional‐order command filtered implementation scheme is presented with the aid of the adaptive backstepping method. The system uncertainties are approximated by neural network. Nonlinear disturbance observer is designed to estimate the bounded disturbances. To overcome the input saturation nonlinearity problem, a smooth function is used to approximate the saturation function. To prevent the violation of the full‐state constraint, a barrier Lyapunov function is introduced in each step of the backstepping process. By selecting proper finite‐time stability criterion, tracking error converges to a small neighborhood of the origin in finite time. Furthermore, by using fractional‐order Lyapunov stability theory, it is demonstrated that all system states remain within given constraints, and other signals of the closed‐loop system are semiglobal uniform ultimate bounded. Finally, the effectiveness of the designed control strategy is verified through two simulation examples.