Fixed-Time Adaptive Tracking Control for Multi-Agent Systems with Input Saturation and Sensor Hysteresis

This paper considers the fixed-time control issue of a class of uncertain nonlinear multi-agent systems with external disturbances under unknown sensor hysteresis and input saturation. The hysteresis phenomenon exhibited by sensors is characterized using the Bouc–Wen model. The radial basis function neural network (RBFNN) is used to approximate the unknown nonlinear dynamics, and two adaptive rules are designed to estimate the uncertain parameters contained in the RBFNN and compensate for the sensor hysteresis, respectively. The backstepping technique is employed to design the distributed adaptive fixed-time control mechanism. The difficulty of controller design is that the exact states of the system cannot be fed back and the singularity issue in fixed-time control must be excluded. The proposed scheme ensures the practical fixed-time stability of the closed-loop system and the boundedness of all system signals. Finally, numerical simulation is conducted to confirm the validity of the theoretical results.