Distributed adaptive three‐dimension formation control based on improved RBF neural network for non‐linear multi‐agent time‐delay systems. Issue 17 (1st November 2019)

• Record Type: Journal Article
• Authors: Zhang, Mingyang; Yu, Xinyi; Ding, Peixuan; Ou, Linlin; Zhang, Weidong
• Is Part Of: IET control theory & applications. Volume 13:Issue 17(2019)
• Page Start: 2758
• Page End: 2765
• Publication Date: 2019-11-01
• Subjects: adaptive control -- control system synthesis -- multi‐agent systems -- delay systems -- radial basis function networks -- multi‐robot systems -- Lyapunov methods -- nonlinear control systems -- observers -- reduced order systems -- neurocontrollers -- stability -- uncertain systems -- linear systems; Lyapunov function -- controller design -- nonlinear dynamic uncertainties -- nonlinear multiagent time‐delay systems -- linear reduced‐order observers -- absolute state error -- RBF neural network -- radial basis function neural networks -- distributed adaptive three‐dimension formation control -- stability; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2018.6130
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : Based on the improved radial basis function (RBF) neural networks, the distributed three‐dimension formation control scheme in the presence of dynamic uncertainties is studied for non‐linear multi‐agent systems with time delay. A virtual leader which tracks the desired signal is followed by all agents adaptively. Linear reduced‐order observers are designed on the basis of absolute and local state errors of each agent. The local state error and absolute state error are generated between neighbouring agents and each individual agent in formation, respectively. The time delay for each agent in the formation can be offset by designing a Lyapunov function, which can simplify the controller design. To deal with non‐linear dynamic uncertainties and unavoidable disturbance, improved RBF neural networks are employed. In comparison with traditional RBF neural networks, improved RBF neural networks can provide better convergence performance. Subsequently, the formation controller is designed and the stability of the systems is validated by using a new Lyapunov function. Numerical simulation is conducted to demonstrate the effectiveness of the proposed method for non‐linear multi‐agent time‐delay systems.