“…The communication topology of USVs with a leader and N followers is described by the directed graph G = (V, E) [44][45][46] , where node set V = {0, 1, • • • , N} with "0" being the leader and "1, 2, • • • , N" standing for the followers. E = {(j, i) : i, j ∈ V, i = j, i = 0} is a set of edges, where ordered edge (j, i) implies that node i can receive the information from node j, and node j is a neighbor of node i.…”
In this paper, the formation control problem is investigated for a team of uncertain underactuated surface vessels (USVs) based on a directed graph. Considering the risk of collision and the limited communication range of USVs, the prescribed performance control (PPC) methodology is employed to ensure collision avoidance and connectivity maintenance. An event-triggered mechanism is designed to reasonably use the limited communication resources. Moreover, neural networks (NNs) and an auxiliary variable are constructed to deal with the problems of uncertain nonlinearities and underactuation, respectively. Then, an event-triggered formation control scheme is proposed to ensure that all signals of the closed-loop system are uniformly ultimately bounded (UUB). Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.
“…The communication topology of USVs with a leader and N followers is described by the directed graph G = (V, E) [44][45][46] , where node set V = {0, 1, • • • , N} with "0" being the leader and "1, 2, • • • , N" standing for the followers. E = {(j, i) : i, j ∈ V, i = j, i = 0} is a set of edges, where ordered edge (j, i) implies that node i can receive the information from node j, and node j is a neighbor of node i.…”
In this paper, the formation control problem is investigated for a team of uncertain underactuated surface vessels (USVs) based on a directed graph. Considering the risk of collision and the limited communication range of USVs, the prescribed performance control (PPC) methodology is employed to ensure collision avoidance and connectivity maintenance. An event-triggered mechanism is designed to reasonably use the limited communication resources. Moreover, neural networks (NNs) and an auxiliary variable are constructed to deal with the problems of uncertain nonlinearities and underactuation, respectively. Then, an event-triggered formation control scheme is proposed to ensure that all signals of the closed-loop system are uniformly ultimately bounded (UUB). Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.
“…In missile control systems, the disturbances consisting of unmodeled dynamics, external wind, and the effects of time-varying parameters often directly affect the states of systems. The disturbances in these practical applications are called mismatched disturbances (Guo and Kang, 2021;Li et al, 2017;Wang et al, 2017Wang et al, , 2021Xiao et al, 2020), which are in the different channels from control inputs and are difficult to be directly conquered by feedback control. Just for this reason, dealing with mismatched disturbances is more challenging than matched disturbances.…”
In this paper, distributed fixed-time average-tracking problem is discussed for second-order multi-agent systems (MASs) with mismatched and matched disturbances. To address this issue, we first propose a novel distributed fixed-time average estimator for each agent to estimate the average value of multiple time-varying reference signals (TVRSs). Then, to tackle with the case in the presence of external mismatched and matched disturbances, a disturbance observer is constructed to estimate them in a fixed time. Finally, based on the disturbance observer, a fixed-time average-tracking protocol is designed to drive each agent to track the average value of multiple TVRSs within a fixed time. Simulation experiments are conducted to verify the effectiveness of the proposed fixed-time anti-disturbance average-tracking algorithm.
“…Therefore, the finite-time control problem has attracted much attention. [25][26][27] Based on the finite-time stability criterion, an adaptive finite-time fuzzy control strategy was proposed to solve the finite-time tracking problem in Reference 25. In order to improve the convergence rate of containment errors of MASs, an adaptive finite-time containment control scheme was developed in Reference 26.…”
Section: Introductionmentioning
confidence: 99%
“…The finite/fixed-time control may cause the "singularity" problem in backstepping recursive design. In the existing works, 25,26 the problem has been solved by using filters. However, using filters will increase the number of online adjustment parameters.…”
Section: Introductionmentioning
confidence: 99%
“…Different from the infinite‐time control method, the finite‐time control method can improve the transient performance of system. Therefore, the finite‐time control problem has attracted much attention 25‐27 . Based on the finite‐time stability criterion, an adaptive finite‐time fuzzy control strategy was proposed to solve the finite‐time tracking problem in Reference 25.…”
Summary
This article studies the containment control problem for uncertain nonlinear multi‐agent systems. To solve this problem, a performance‐oriented fuzzy adaptive algorithm is proposed. By constructing a new Lyapunov function and using the predefined accuracy functions, the algorithm ensures the negative‐definite property of the time derivative of the Lyapunov function while guaranteeing that the containment errors converge to a user‐defined interval. Different from most existing results, the control algorithm proposed in this article realizes the negative‐definite property of the time derivative of Lyapunov function without adjusting parameters. Then, an event‐triggered mechanism is introduced to reduce the communication burden between the controller and the actuator. Moreover, by using the singularity‐avoidance functions, a fixed‐time fuzzy nonsingular controller is constructed to avoid the possible “singularity” problem in the backstepping recursive design. Based on the fixed‐time stability criterion, it is proved that all output signals converge to a predefined interval in a fixed time. Finally, several simulation results further demonstrate the effectiveness of the presented control strategy.
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