Distributed Adaptive Fuzzy Output Consensus Control of Nonlinear Multi-agent Systems with Different Measurement Sensitivities

This paper investigates the leader-following consensus problem for nonlinear multi-agent systems with unknown states and unknown output measurement sensitivities. Different from the existing results, the output measurement sensitivities and their signs are totally unknown and different for all agents in systems. In the paper, firstly, we design novel distributed observers with fuzzy K-filters to reconstruct the states for each agent, solving the problem of unknown states. Secondly, by combining the back-stepping method and adaptive technique, we construct adaptive distributed output-feedback controllers and adaptive laws. Besides, we adopt the fuzzy logic systems to approximate the nonlinearities in multi-agent systems. Based on the Lyapunov stability theory, we prove all the agents can achieve a consensus, and all the signals are bounded. Finally, a UAVs simulation example is adopted to illustrate the feasibility of our proposed algorithm.