Adaptive Containment Control for Nonlinear Multi-Agent Systems with Input Quantization and Sensor Faults

This study investigates the adaptive containment control problem for a class of nonlinear multi-agent systems with input quantization and sensor faults. A state observer and a radial basis function neural network are respectively employed to estimate unmeasurable states and approximate unknown nonlinear functions. An absolute cubic Lyapunov function is designed to compensate for the influence of sensor faults on the systems. A filter is introduced to reduce computational complexity. Adaptive laws are developed to update the estimates of uncertain dynamic parameters, fault coefficients, and the filter-error compensation term. A distributed adaptive control scheme is proposed to ensure that all followers converge to the convex hull formed by the leaders. The stability of the closed-loop system is strictly proved based on stability theory, and the effectiveness of the proposed control method is verified by numerical simulation.