Finite-time output feedback leader-following consensus for high-order time-varying nonlinear multi-agent systems

This article studies the event-triggered output feedback consensus problem for a class of high-order uncertain nonlinear multiagent systems with totally unknown non-identical control directions. Firstly, the reduced-order filters are constructed by utilizing the dynamic gain technique to compensate the system uncertainties and unmeasurable states of the followers. Based on the filter states and output information, the asymptotic output consensus result is guaranteed with the developed distributed adaptive output feedback control protocol, while the inherent issue "complexity explosion" in backstepping design is avoided. Then, to solve the interaction of multiple Nussbaum functions in a single inequality, the Nussbaum functions with different frequencies are adopted, with which the unknown directions of multiagent system are no longer limited in identical. In addition, we propose a dynamic triggering strategy with time-varying threshold parameters to reduce the updates of controller without Zeno phenomenon. Finally, simulation results are provided to illustrate the effectiveness of theoretical algorithm.

Adaptive consensus for high-order unknown nonlinear multi-agent systems with unknown control directions and switching topologies

In this article, for second-order multiagent systems with uncertain disturbances, the finite-time leader-follower consensus problem has been investigated. First, by considering that the leader's states are only available to part of the followers, a distributed estimator is constructed to estimate the state tracking errors between the leader and each follower. Then, an estimator-based control scheme is proposed under the event-triggered strategy to achieve finite-time leader-follower consensus. Besides, the event-triggered intervals are with a positive lower bound such that the Zeno behavior can be avoided. Note that the system is discontinuous under the event-triggered mechanism; thus, a nonsmooth analysis is performed. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

This brief investigates a distributed optimization problem for high-order strict-feedback nonlinear multiagent systems. A virtual system is built for each agent using a distributed proportional-integral (PI) optimization algorithm to estimate the global optimal solution online. The estimate is then input into a prefilter to generate an alternative estimate and its high-order derivatives. Based on these signals, a backstepping controller is used to make the actual system track the alternative estimate asymptotically, which thereby realizes exact global consensus optimization. Compared to the existing result, the proposed algorithm is fully distributed under undirected topologies with much weaker assumptions on local and global objective functions, lower computation and communication cost. Exact global optimal consensus can also be reached in the cases with directed topologies and intermittent communications. The effectiveness of the method is verified by simulations.

This study is devoted to investigating the fixed-time consensus disturbance rejection problem for a class of high-order nonlinear multi-agent systems (MASs) with input saturation. A distributed fixed-time state observer (FTSO) is first proposed to reconstruct the leader’s states for each follower. Based on the estimated values, a fixed-time consensus protocol is designed via backstepping, where a fixed-time disturbance observer (FTDO) is used to provide the robustness against the external disturbances. The violation of the input saturation is prevented by introducing an auxiliary variable and the problem of “explosion of terms” suffered by the conventional backstepping design is also successfully avoided by a fixed-time differentiator. Detailed convergence results are presented by leveraging Lyapunov stability theory. Finally, the effectiveness of the proposed approach is numerically validated through simulation results.

Fixed-time resource allocation algorithm for high-order MIMO nonlinear multi-agent systems

Adaptive leader-following consensus for a class of higher-order nonlinear multi-agent systems with directed switching networks

The guaranteed-performance consensualization for high-order linear and nonlinear multiagent systems with switching topologies is respectively realized in a completely distributed manner in the sense that consensus design criteria are independent of interaction topologies and switching motions. This paper first proposes an adaptive consensus protocol with guaranteed-performance constraints and switching topologies, where interaction weights among neighboring agents are adaptively adjusted and state errors among all agents can be regulated. Then, a new translation-adaptive strategy is shown to realize completely distributed guaranteed-performance consensus control and an adaptive guaranteed-performance consensualization criterion is given on the basis of the Riccati inequality. Furthermore, an approach to regulate the consensus control gain and the guaranteed-performance cost is proposed in terms of linear matrix inequalities. Moreover, main conclusions for linear multiagent systems are extended to Lipschitz nonlinear cases. Finally, two numerical examples are provided to demonstrate theoretical results.

Neuro-adaptive non-singular terminal sliding mode control for distributed fixed-time synchronization of higher-order uncertain multi-agent nonlinear systems

Adaptive cooperative control for high-order nonlinear multiagent time-delay systems using barrier functions

Sampled-data leader-following consensus for nonlinear multi-agent systems with Markovian switching topologies and communication delay

This paper deals with the leader-following consensus problem for a class of second-order nonlinear multi-agent systems by output feedback. The communication topology is characterized by a Markovian switching graph. Firstly, an input-driven observer is introduced to estimate the consensus error of each follower agent. Then, a cooperative nonlinear control law is constructed using the relative output information between neighboring agents by employing the backstepping methodology, which achieves leader-following consensus in mean square sense. Compared with the existing results, the nonlinear functions are required to satisfy polynomial growth condition rather than globally Lipschitz growth or Lipschitz-like growth condition. A numerical example is given to illustrate the theoretical results.

In this paper, the leader–follower consensus problem is investigated for a class of lower-triangular nonlinear multi-agent systems with unknown measurement sensitivities. By developing a dual-domination gain method, a distributed compensator is proposed for each follower by utilising the output information of the follower and its neighbour agents. Based on the compensator, an output feedback control law is designed to achieve consensus. These two gains are used to deal with the unknown measurement noises and nonlinear terms, respectively. Then the consensus problem is transformed into a stability problem by introducing an appropriate state transformation. Based on the Lyapunov stability theory, it is proved that the states of the leader and followers can achieve consensus asymptotically. In the end, numerical simulations are provided to verify the correctness of the proposed consensus algorithm.

This paper presents a novel leader-follower consensus control method for a special class of nonlinear multi-agent mechanical systems in the presence of control input saturation and external disturbances, and this class includes robot system dynamics with a wide range of potential applications in industry. In the innovative technique, one of the agents is selected as the leader and the other agents are guided in such a way that the entire states of the system can reach a consensus within the transient limits of the determined performance. Due to the presence of disturbance in most practical systems, the effect of limited disturbance has been evaluated in the consensus control method and actuator saturation is included in the design process of the control technique. Finally, a terminal sliding mode control method is adapted to ensure the stability of the overall system and fast finite-time leader-follower consensus control. The simulation results of the multi-agent nonlinear robot system in the MATLAB environment, in different scenarios by simultaneously considering the actuator saturation and external disturbance, show the effectiveness of the proposed control method.

Due to the difficulty encountered in dealing with unstructured system dynamics with unmeasured system state variables, this paper presents a novel observer-based neural network finite-time output control strategy for general high-order nonlinear systems (HNSs). The suggested technique is performed based on the backstepping-like control (BSC) scheme with a hybrid nonlinear disturbance-state observer and norm estimation-based radial basis function neural network (RBFNN). This helps not only reduce the number of estimated parameters but also relax the restriction of using inequality when exploiting the norm estimation concept in a conventional way; thus, retaining the same properties of the original system. Therefore, an observer-based finite-time output feedback control is established to deal with the unstructured dynamical behaviors and satisfying the output tracking regulation with the semi-global practically finite-time stability (SGPFS) guaranteed for the closed-loop system. The effectiveness and workability of the proposed algorithm is verified by a numerical simulation on a specific practical application.