Adaptive finite‐time consensus tracking control for nonlinear multiagent systems in nonstrict feedback form with full‐state constraints

Abstract

Summary In this article, the fuzzy adaptive finite‐time consensus tracking control problem for nonstrict feedback nonlinear multiagent systems with full‐state constraints is studied. The finite‐time control based on command filtered backstepping is proposed to guarantee the finite‐time convergence and eliminate the explosion of complexity problem caused by backstepping process, and the errors in the filtering process are compensated by using error compensation mechanism. Furthermore, based on the fuzzy logic systems, the uncertain nonlinear dynamics are approximated and the problem of state variables in nonstrict feedback form is solved by using the property of basis functions. The barrier Lyapunov functions are introduced to guarantee that all system states and compensated tracking error signals are constrained in the designed regions. A simulation example is given to verify the superiority of the proposed algorithm.

Adaptive Finite-Time Consensus Tracking Control for Coopetition Flexible Joint Multi-Manipulators With Full-State Constraints

“…On the other hand, from the definition of χmin in (44), we can also conclude that the directed graph information (d i + b i ) needs to be considered when selecting the π i,s,p value. Finally, referring to [29], we can get that h 0 < σ0 √ N [(κ i,1 ) min − κ0 ], h 0 + ˆ i,s,p < κi,s,p and ξ i,s,p ≤ π i,s,p need to be satisfied in the process of selecting π i,s,p , φ i,s,p , ξ i,s,p , λ i , γ i,s,1,p , γ i,s,2,p . Remark 5: In practical multi-agent control systems, some excellent methods have been proposed due to practical issues such as communication and system mode switching.…”

“…What's more, in some requirements of confrontation and negation, the coopetition system can play its excellent role [25]- [27]. The CFB finite-time method combined with full-state constraints for MASs is investigated in [28], [29], but they do not consider the coopetition system and manipulator. On the contrary, the authors in [30]- [32] solve the bipartite consensus tracking problem for coopetition MASs, but the condition of finite-time convergent and states constraints are not handled.…”

Finite‐time adaptive consensus of stochastic multi‐agent systems with node‐based and edge‐based adaptive law design methods

“…With the change of the system behavior, the parameters of the adaptive controller can change accordingly to adapt to the change of its characteristics, so as to ensure that the performance index of the whole system can achieve satisfactory results. [20][21][22] That is, the adaptive technology can automatically adjust the parameters of the protocols (controllers) to adapt to the changes of environmental conditions or process parameters by measuring the continuous information of the process state, so as to maintain the performance criteria required by the control systems. 19 Therefore, adaptive technology has distinct advantages in the cooperative control of MASs, which not only can make the consensus protocols more robust and flexible, but also can reduce or avoid vibration, undesirable distortion and environmental stochastic noise.…”

“…Meanwhile, adaptive control is a popular control technology based on the unknown parameters of the mathematical model of the system. With the change of the system behavior, the parameters of the adaptive controller can change accordingly to adapt to the change of its characteristics, so as to ensure that the performance index of the whole system can achieve satisfactory results 20‐22 . That is, the adaptive technology can automatically adjust the parameters of the protocols (controllers) to adapt to the changes of environmental conditions or process parameters by measuring the continuous information of the process state, so as to maintain the performance criteria required by the control systems 19 .…”

Distributed adaptive NN control for nonlinear multi‐agent systems with function constraints on states

“…[33][34][35][36][37] However, for the system, the problems to be considered in studying state constraints are more comprehensive and more complex than output constraints. [38][39][40][41][42] The MASs with state constraints is researched to keep all state variables within the constraints. 38 As the agent is affected by the full-state constraints, nonlinear transformation function is structured to deal with the constraints of the system.…”