Command-filtered adaptive containment control of fractional-order multi-agent systems via event-triggered mechanism

Abstract

In this paper, the containment control problem of a class of fractional-order nonlinear multi-agent systems is studied, in which the multi-agent system contains unmeasured states and system nonlinearity. An adaptive neural network backstepping controller combined with event-triggered mechanism is proposed to ensure all followers can converge to the convex hull spanned by the leaders. The command filter is introduced into the proposed fractional-order control system to obtain fractional derivatives for virtual control functions continuously and avoid “explosion of complexity.” From the Lyapunov stability theory, all the signals can remain semi-global uniformly ultimately bounded in the closed-loop system. Numerical example and simulation results confirm the feasibility of the proposed control method.

Fuzzy adaptive containment control for fractional‐order heterogeneous nonlinear multi‐agent systems with mixed time‐varying delays

“…1. Compared with existing results, [10][11][12][13][14][15][16][17][18][28][29][30][41][42][43] the studied FO MASs are heterogeneous, including more realistic scenarios, such as unknown time-varying state delays and distributed time-varying delays. The presence of time delays, especially distributed time delays, poses significant challenges to controller design and stability analysis.…”

“…The results obtained in literature, 10–18,41–43 do not include delay information. Literature 28,29 consider constant delays, while literature 30 considers time‐varying delays, but none of them consider distributed time‐varying delays.…”

“…Adaptive neural network is a control method that using neural networks that can automatically adjust their parameters to adapt to unknown nonlinear function terms, leading to better handling of control problems of nonlinear systems and improvement of control algorithm performance and robustness. Recent literature 17 proposed a combination of an adaptive neural network controller and an event‐triggered mechanism, while Reference 18 developed a distributed adaptive neural network bipartite containment control method under the backstepping structure, achieving finite‐time convergence. These studies illustrate the efficacy of adaptive neural network techniques in solving control issues pertaining to complex nonlinear systems with unknown nonlinear function terms.…”

“…Drawing from the aforementioned discussion, this paper combines the theory of IT2 FLS and adaptive methods to study the CC problem of a class of unknown nonlinear FO heterogeneous MASs with time‐varying state delays and distributed time‐varying delays. The primary contributions are listed as follows: Compared with existing results, 10–18,28–30,41–43 the studied FO MASs are heterogeneous, including more realistic scenarios, such as unknown time‐varying state delays and distributed time‐varying delays. The presence of time delays, especially distributed time delays, poses significant challenges to controller design and stability analysis.…”

Observer-based distributed convex optimization of bipartite containment control for higher order nonlinear uncertain multi-agent systems

“…The bipartite containment of fractional order system is analyzed and a controller with good control performance is proposed by Chen and Yuan. 25 The control analysis of consistency optimization problem is carried out by Yang et al 26…”