Observer-Based Adaptive Fixed-Time Control of Multi-Agent Systems with Input–Output Quantization and Sensor Faults

This paper investigates the issue of fuzzy adaptive fixed-time tracking control for a class of strict feedback nonlinear multi-agent systems with input–output quantization and sensor faults. Based on the quantized output signals and the estimated fault parameters, a state observer is designed to estimate the unmeasurable states. The uncertain nonlinear dynamics are parameterized by employing the fuzzy logic system. To compensate for the impact of sensor faults on control performance, an absolute cubic Lyapunov function is constructed. A finite-time convergence filter is used to solve the problem of “complexity explosion”. The estimations of the uncertain dynamic parameters, fault parameters, and filter error compensation parameters are adjusted by the designed adaptive laws. A distributed fuzzy adaptive control scheme based on the backstepping technique is proposed such that all following agents can track the reference signal in a fixed time. The closed-loop system is guaranteed to be practically fixed-time stable through stability analysis. Finally, the effectiveness of the theoretical results is verified by simulation.