In this paper, we study the robust output regulation of linear systems by an event-triggered output feedback control law. We first summarize the solvability of the linear robust output regulation problem by the internal model design. Then we implement this continuous-time control law by an event-triggered output feedback control law together with an output-based triggering mechanism. We show that, under the same solvability conditions as those for the linear robust output regulation problem, the robust output regulation problem can also be solved by an event-triggered output feedback control law without incurring Zeno phenomenon. Further, we show that, it is possible to solve the robust output regulation problem practically while preventing the inter-event time from approaching zero as time tends to infinity.

In this article, the quasi-consensus control problem is investigated for a class of stochastic nonlinear time-varying multiagent systems (MASs). The innovation points of this research can be highlighted as follows: first of all, the dynamics of the plant are stochastic, nonlinear, and time varying, which resembles the natural systems in practice closely. Meanwhile, an energy harvesting protocol is put forward to collect adequate energy from the external environment. Second, as a generalization of the existing result, the ultimate control objective is quasi-consensus in a probabilistic sense, that is, designing a distributed control protocol in order that the probability of centering the allowable region for the states of each agent is larger than some predetermined values. Third, the MASs are subject to false data-injection (FDI) attacks, and a more general multimodal FDI model is proposed. On the basis of the probabilistic-constrained analysis technique and the recursive linear matrix inequalities (RLMIs), sufficient conditions are provided to guarantee the probabilistic quasi-consensus property. To derive the controller gains, an optimal probabilistic-constrained algorithm is designed by solving a convex optimization problem. Finally, two examples are provided to substantiate the validity of the proposed framework.

In this article, the cooperative output regulation problem (CORP) for unknown linear multiagent systems (MASs) is studied on the basis of a data-driven approach. Different from existing literature, first, the proposed method does not require prior knowledge of the system matrices and the leader system where the control gains are designed using input and state data. Next, the solution to the unknown regulator equations is derived using the data informativity approach. Then, to speed up the convergence rate, a distributed deadbeat control law is proposed for the MASs. For the purpose, the leader's system matrix is identified according to the measured data, and then a distributed observer is employed to estimate the state of the leader system in a distributed manner. Furthermore, the CORP is converted to a stabilization problem by using the data-based solution of the regulator equations. On the basis of the distributed observer, a data-driven deadbeat control law is designed in the context where the system matrices of the plant are completely unknown. It is guaranteed that the tracking error for each agent can converge to the origin asymptotically. Finally, an example is conducted to illustrate the effectiveness of the proposed data-based framework.

This technical article conducts the research on the cooperative output regulation problem (CORP) of linear heterogeneous discrete-time multiagent systems (MASs) with the constraints of unknown dynamics. On the basis of the data-driven scheme, a state-based distributed control protocol is constructed to achieve the asymptotic tracking for leader system’s reference signals while getting rid of external disturbances. Furthermore, the result is extended to the output-based case based on the least square approach. In order to cope with the unknown leader system, an observer is put forward to estimate the leader’s state after obtaining the system matrices for the exosystem by utilizing the persistently exciting data. To further overcome the difficulties of the unknown dynamics of each follower, input-output relationship is constructed to infer the system matrices. Moreover, the solution to the unknown regulator equations is provided by solving the data-dependent matrix equations. Ultimately, the control gains are derived by solving the convex optimization problem. The affirmation of the whole design scheme is demonstrated by two simulation cases.

In this note, the cooperative output regulation problem of general heterogeneous linear multi-agent systems with binary constraints is studied. To speed up the convergence rate, the binary distributed observer is proposed only utilizing sign of the relative state instead of using full relative information. Under some mild conditions, the binary distributed observer can estimate the state of the leader system exponentially in finite time sense. The leader system can generate various reference signals such as step functions, sinusoidal functions or their combinations. Furthermore, both state and output feedback control protocols are synthesized to solve the problem. An illustrative example is presented to support the feasibility of the proposed framework.