Observer-Based Adaptive Scaled Tracking Control for Nonlinear MASs via Command-Filtered Backstepping
Xiujuan Zhao, Shiming Chen, Zheng Zhang, Yuanshi Zheng
Abstract
This article focuses on the problem of adaptive scaled consensus tracking control for uncertain nonlinear multiagent systems (MASs) subjected to unknown mixed control gains, input delays, and external disturbances. First, a new form of nonlinear MAS is presented by linear state transformation. Second, a state observer based on adaptive radial basis function neural networks is developed to estimate the unmeasured states. A command filter control scheme is deployed to address the problem of increased sharply complexity derived from the conventional backstepping design with the increase of the system order, and the filtered error is compensated by the error compensation mechanism. Third, by taking advantage of the Lyapunov–Krasovskii functionals, a compensation control strategy is intended to exclude the impact of input delays. In addition, a Nussbaum-gain function is used to deal with the problem of uncertain control direction. An adaptive output feedback control approach is raised to construct the scaled consensus tracking control protocol, error compensating signals, and adaptive laws. It is proved that the tracking errors are driven to a small residual set, and all the signal variables are bounded in the closed-loop system. Finally, the effectiveness of the proposed approach is verified by two numerical simulations.