A Dynamic Gain Method for Fuzzy Distributed Optimization of Nonlinear Multiagent Systems

Abstract

This article proposes a new dynamic gain method for the fuzzy distributed optimization control of strict-feedback nonlinear multiagent systems (MASs). This method eliminates the need for the backstepping design in existing distributed optimization schemes and in the application of fuzzy logic systems, thereby avoiding the inherent iterative design and complexity explosion issues. The controller resulting from this method has a simple quasi-linear structure. Notably, the non-parametric uncertainties considered make the system under investigation more general than those in existing results, where systems’ nonlinear functions are often assumed to be known, limited to linear growth conditions, or subject to parametric uncertainties. To handle these non-parametric uncertainties, a method integrating dynamic gains with fuzzy logic systems is employed. Furthermore, a novel analytical approach based on LaSalle’s theorem is developed, which relaxes the constraints on local cost functions in the existing literature. Under the proposed control scheme, the distributed optimization consensus is achieved. The effectiveness of the proposed scheme is validated through two simulation examples.