Adaptive Fuzzy Control for Multi-Agent Systems with Unknown Measurement Sensitivity via a Simplified Backstepping Approach

This paper investigates the adaptive tracking control problem for a class of nonlinear multi-agent systems with unknown measurement sensitivity using a simplified backstepping approach. By introducing Nussbaum gain, a new approach is proposed that effectively relaxes the restrictive condition that the unknown measurement sensitivity be within a particular range. A simplified backstepping approach is then presented to alleviate the structural complexity and high computational costs. It is proven that the tracking error converges to a small neighborhood of the origin and that all of the signals are bounded. Finally, simulation analysis is conducted to verify the effectiveness of the proposed control strategy.