Adaptive fuzzy singularity-free finite-time optimal control for nonlinear pure-feedback multiagent systems

In this paper, we investigate the adaptive fuzzy singularity-free finite-time optimal consensus problem for nonlinear pure-feedback multiagent systems (MASs). For purpose of achieving the optimized control, the fuzzy approximation-based reinforce-ment learning is employed under critic-actor architecture. By virtue of Butterworth low-pass filter, a distributed adaptive finite-time optimal consensus method is developed for nonlinear pure-feedback MASs, which solves algebraic loop problem produced in the construction of the optimal controller. Most importantly, to be free of singularity, we design a new dynamic filtering optimized backstepping method to avoid the differentiations of virtual optimal controllers, and the errors between first-order filter signals and virtual optimal controllers can be counteracted by designing the smooth robust compensators for the first time. It is shown that, with the developed adaptive finite-time optimal consensus control, both the optimal consensus tracking performance and finite-time convergence for the closed-loop systems are ensured. Three simulation examples are presented to verify the effectiveness of the proposed approach.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this paper, the adaptive fuzzy singularity-free finite-time optimal consensus problem is investigated for nonlinear pure-feedback multiagent systems. To achieve optimized control, the fuzzy approximation-based reinforcement learning is employed. A distributed adaptive finite-time optimal consensus method is developed by using Butterworth low-pass filter to solve the algebraic loop problem. A new dynamic filtering optimized backstepping method is designed to avoid differentiations of virtual optimal controllers. The effectiveness of the proposed approach is verified through three simulation examples.