Distributed consensus tracking control for nonlinear multiagent systems with state delays and unknown control coefficients

2021 ◽  
Author(s):  
Wenting Wang ◽  
Zhengrong Xiang
Keyword(s):  
Multiagent Systems ◽  
Tracking Control ◽  
Distributed Consensus ◽  
State Delays ◽  
Consensus Tracking ◽  
Unknown Control Coefficients ◽  
Unknown Control ◽  
Control Coefficients

scholarly journals Adaptive tracking control for uncertain nonlinear systems subject to unknown control coefficients, state constraints, and input saturation

2021 ◽  
Author(s):  
Chunxiao Wang ◽  
Lu Qi ◽  
Yan Zhao ◽  
Jiali Yu
Keyword(s):  
Tracking Control ◽  
State Constraints ◽  
Input Saturation ◽  
Approximation Error ◽  
Tracking Performance ◽  
Dynamic Surface ◽  
Unknown Control Coefficients ◽  
Unknown Control ◽  
Control Direction ◽  
Control Coefficients

Abstract This article is committed to studying the tracking control problem for a class of uncertain nonlinear system with unknown control coefficients. The system is subject to full state constraints, input saturation constraint, and external disturbances simultaneously. By introducing a hyperbolic tangent function to approximate the saturated input function, the sharp corner caused by the input saturation is avoided. In the meanwhile, an auxiliary system is constructed to compensate the resulting approximation error. By using the barrier Lyapunov function (BLF) based adaptive backsteping control, the Nussbaum-type adaptive controllers are constructed for the augmented system with unknown control direction. It not only ensures the system states are always within the constrained range, but also guarantees the tracking performance of the system, no matter whether the control direction of the system is known or not. Meanwhile, dynamic surface control (DSC) is used in the controller design, which avoids ”computation explosion” caused by the repeated derivation of virtual control law. Aiming at the nonparametric uncertainty of the system, a common adaptive law is designed by combining the unknown constant bounds of the external disturbance with the error term caused by input saturation estimation. It improves the tracking performance of the system and reduces the burden of the controller greatly. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control scheme in three scenarios.

scholarly journals Adaptive Fuzzy Consensus Tracking Control for Nonlinear Multiagent Systems with Time-Varying Delays and Constraints

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jie Lan ◽  
Tongyu Xu
Keyword(s):  
Multiagent Systems ◽  
Tracking Control ◽  
Tracking Error ◽  
Time Varying ◽  
Feedback Systems ◽  
Distributed Consensus ◽  
Fuzzy Logic Systems ◽  
State Time ◽  
Adaptive Fuzzy ◽  
Consensus Tracking

This paper proposes an adaptive fuzzy distributed consensus tracking control scheme for a class of uncertain nonlinear dynamic multiagent systems (MASs) with state time-varying delays and state time-varying constraints. The existing controllers with Lyapunov–Krasovskii functions (LKFs) were not suitable to address time-varying delays and time-varying constraints in nonlinear MASs simultaneously. State constraints further increase the difficulty of controller design and stability analysis, especially for nonstrict feedback systems. Fuzzy logic systems (FLSs) tackle the approximation of unknown dynamics functions and parameters. Especially when the distributed consensus tracking error is infinitely close to the origin, although there is no singular value, it would lead to the rapid growth of control rate or uncontrollability. Constructing appropriate piecewise functions can effectively avoid the above occurrence and accelerate convergence. Based on Lyapunov stability theory and algebraic graph theory, the constructed tracking control can ensure states within defined time-varying constraint bounds and eliminate the influence of time delays. All signals in closed-loop systems can be guaranteed semiglobally uniformly ultimately bounded (SUUB). Finally, the validity of the theoretical method is verified by the simulation.

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