Adaptive Fuzzy Distributed Optimization for Uncertain Nonlinear Multiagent Systems
Yukan Zheng, Yuan‐Xin Li, Choon Ki Ahn
Abstract
The adaptive fuzzy distributed optimization problem of nonlinear multi-agent systems (MASs) with unknown nonlinearities and uncertain disturbances is explored in this paper. The unknown nonlinearities in the system model are determined by incorporating fuzzy-logic systems. Rather than using the existing known gradient values of local objective functions, this paper further takes into account the measured gradient values that are based on the output information. The bounded estimation method and well-defined smooth functions are used to account for the effects of uncertainties. To ensure that the output can asymptotically converge to the optimal solution, a new adaptive fuzzy distributed optimization controller is proposed using the Lyapunov function method and the adaptive backstepping technique. To demonstrate the effectiveness of the developed distributed optimization technique, two practical examples are used.