In this paper, the output consensus tracking problem is investigated for heterogeneous multi-agent systems (MASs) with asynchronous sampled communication, where only local output of the leader is required and the local output function contains an unknown parameter. A distributed adaptive observer is designed for recovering the leader’s state and unknown parameter, which has three features: (i) two predictors are contained in the observer to recover the output injection and the consensus error separately; (ii) by designing proper gain matrices, the state estimation is decomposed into the detectable part merely using the sampled local output of the leader and the undetectable part only using sampled information of the neighbors; (iii) the negative effect of the parameter estimation errors is offset by adding complementary terms with coupled auxiliary variables into the observer. Based on the observer, the control scheme is provided to realize the exponential output consensus tracking. It is shown that the maximal sampling period for the follower to receive local output from the leader is independent of the neighbors.

This paper investigates a dynamic event-triggered distributed observer for linear systems including two groups of local observers: one can access the local outputs and another cannot. By the detectability decomposition, the proposed observer contains a detectable sub-state (DSS) observer and a distributed undetectable sub-state (USS) observer. The dynamic event-triggered mechanism (DETM) for the outputs only requires a copy of the DSS observer with low dimension. Besides, only the USS estimate is transmitted to the neighbors which can reduce the communication burden. Positive minimum inter-event times are prescribed previously in the DETMs, and piece-wise dynamics for internal dynamic variables are employed. By modeling the error systems as hybrid systems, the exponential stability of the proposed observer is assured with the utilization of time-triggering method and event-triggering method.

In this article, the adaptive consensus tracking control is developed for uncertain multiagent systems with time-varying state delay in the case that leader’s state is accessible at sampling instants. By proposing a distributed sampled observer with hybrid form, adaptive tracking controller with the complementary term is designed for first-order multiagent systems, and then is extended to high-order multiagent systems with the aid of dynamic surface control. Through the complementary term, the effects of parameter estimation error as well as dynamical terms with time-varying delays are eliminated and thus less conservative condition on time delays is required. It is proved that, under criteria in terms of linear matrix inequalities (LMIs), tracking error and estimation error exponentially converge to zero for first-order systems, and to a sufficiently small neighborhood of zero for high-order systems.

This paper presents two methods for finite-time topology identification for the complex spatio- temporal networks with coupling time delay. By introducing the auxiliary systems, a relationship between the unknown topology and two measurable matrix signals is developed. Based on the relationship, one method of identifying the topology is to compute the invertibility of the matrix. Besides this method, an adaptive law is developed to infer the topology online in finite time. The proposed methods do not require the differentiability of the time-varying delay. Furthermore, the methods can also be applied to complex spatio-temporal networks with unknown system parameters. Finally, an illustrative example is provided to show the effectiveness of the proposed methods.

This paper addresses the sampled-data state estimation problem for complex networks using partial nodes’ measurements. A hybrid observer network with partial control is developed to estimate the state information. The key point of the hybrid observer network is that the state observer network is continuous-time by introducing an output predictor. Besides, the hybrid observer only requires a fraction of nodes’ sampled measurements with partial control technique. It reduces the state estimation cost and improves the estimation effectiveness. Some criteria are developed to guarantee that the proposed observer network is an exponential observer. Finally, simulation example validates the proposed approach.