Distributed Adaptive Control for Asymptotically Consensus Tracking of Uncertain Nonlinear Systems With Intermittent Actuator Faults and Directed Communication Topology

Abstract

In this paper, we investigate the output consensus tracking problem for a class of high-order nonlinear systems with unknown parameters, uncertain external disturbances and intermittent actuator faults. Under directed topology condition, a novel distributed adaptive controller is proposed. The common time-varying trajectory is allowed to be totally unknown by part of subsystems. Therefore, the assumption on linearly parameterized trajectory signal in most literatures is no longer needed. To achieve the relaxation, extra distributed parameter estimators are introduced in all subsystems. Besides, to handle the actuator faults occurring possibly infinite times, a new adaptive compensation technique is adopted. It is shown that with the proposed scheme, all closed-loop signals are globally uniformly bounded and asymptotically output consensus tracking can be achieved.

Leader tracking control for heterogeneous uncertain nonlinear multi-agent systems via a distributed robust adaptive PID strategy

“…the computational burden. Indeed, according to the metric proposed in [30], since the proposed control strategy involves a limited amount of time-varying information for computing the adaptive control, our approach is able to save communication channel bandwidth and to reduce the computational burden w.r.t. the current related literature (see e.g., [21,50,62] and the references therein).…”

“…Differently from the technical literature, the proposed control strategy involves a limited amount of time-varying information for the computation of adaptive control actions. Thus, according to the metric in [30], it is able to save communication channel bandwidth and to reduce the computational burden.…”

“…Note that to deal with the derivative action, we do not need the exploitation of the descriptor transformation [40]; -concerning the heterogeneous nonlinear MAS framework, the proposed approach does not require any assumption about the leader/follower agents dynamics, e.g., boundedness/ Lipschitz nonlinear vector field [21,50,55,58], QUAD assumption [36], or complex adaptive neural-network-based procedure for a specific linear parametrization of leader dynamics [17]; -there is no need of exploiting distributed observers to estimate the leader states or obtaining the solutions of regulator equations, thus making the control design and implementation easier with respect to the current approaches proposed for dealing with heterogeneity in the linear field; -the proposed approach allows guaranteeing the asymptotic convergence of the tracking error differently from other adaptive approaches in the technical literature (as analyzed in [62]). In addition, for the achievement of this control purpose, differently from the aforementioned backstepping or neural network techniques, our approach requires less computational burden according to the metric defined in [30].…”

Adaptive time‐varying formation control of uncertain Euler–Lagrange systems with event‐triggered communication

“…In the last two decades, some significant research topics on coordination control of multiagent systems have been widely investigated, such as leaderless consensus, 1-3 leader-following consensus, [4][5][6][7] and formation control. 8,9 Communication plays a crucial role in the coordination control, which is the bridge of information exchange among connected agents.…”

Distributed adaptive consensus tracking for uncertain high‐order nonlinear multiagent systems with event‐triggered communication

“…Except for robust control, adaptive control is an alternative tool to handle system uncertainties due to the ability of on‐line estimation for uncertain parameters 19 . In currently available results, many feasible distributed adaptive control schemes have been proposed in the works of Wang et al 7,9 and Long et al 20 Note that these consensus algorithms are closely dependent on continuous communication among neighboring agents. However, event‐triggered adaptive consensus algorithms are still limited.…”