In this paper, we study the data-drivenH∞ optimal control problem for output synchronization of heterogeneous multi-agent systems via a computationally efficient reinforcement learning (RL) method. Unlike the existing RL control results which solve for the optimal controller through the vectorization and Kronecker product (VKP) operations where the large size of the Kronecker product increases computational complexity, a computationally efficient data-driven RL control method is studied in this paper. First, the optimal output synchronization problem is transformed into anH∞ optimal control problem by constructing an internal model-based distributed control protocol. Then, the corresponding algebraic Riccati equation for theH∞ optimal control is reformulated as a generalized Sylvester-transpose matrix equation (GSTME) at each iteration. To solve the GSTME, thematrixform of the conjugate gradient least squares algorithms that do not require the VKP operations is proposed based on the system dynamics and measurement data of multi-agent systems, respectively. Furthermore, the initial stabilizing gain required for policy iteration learning is proposed based on a data-based solution method. Finally, the feasibility of the designed algorithms is demonstrated through a numerical simulation, and its better computational efficiency compared to existing methods is shown.

In this paper, a data-based containment control method is proposed for multi-agent systems where the system parameters of both leaders and followers areheterogeneousandunknown. Based on the collected data from the leader systems, the least square method is first utilized to identify the parameters of the leaders. Then, the data-based adaptive distributed compensator is designed for each follower to estimate the system parameters and the states of theheterogeneousleaders. Meanwhile, the collected data from follower systems is used to construct the local observer and establish the data-based linear matrix inequalities to determine the observer gains and feedback gains. On the basis of the collected data from leader and follower systems, the data-based adaptive solutions to the output regulation equations are provided. Furthermore, the data-based containment control protocols are designed to realize that the outputs of the followers converge to the convex hull formed by the unknown heterogeneous leaders. Finally, the effectiveness of the proposed data-based control scheme is illustrated by the simulation of the numerical example and the interconnecting RLC circuits.

This article investigates the robust data-driven containment control problem for heterogeneous multiagent systems where the system dynamics of both the leaders and the followers are unknown. During the data collection phase, the follower systems are disturbed by unmeasurable but bounded noises. A data-based linear matrix inequality condition is first constructed from the noisy data to determinate the feasible feedback gain for each follower. Then, the distributed observer which is independent of the system matrix of the leaders is designed to estimate the convex hull of the leaders. Moreover, the approximate solution to the linear matrix equation for heterogeneous system is solved with bounded approximate error where the noisy data of each follower and the normal data of arbitrary leader is utilized. Based on the proposed feedback gain and observer as well as approximate solution, the robust data-driven control protocol is provided to guarantee the uniform boundedness of containment error. Finally, a numerical example and a multivehicle model are given to verify the effectiveness of the designed containment control protocol.

The distributed dissipative state estimation issue of Markov jump genetic regulatory networks subject to round-robin scheduling is investigated in this paper. The system parameters randomly change in the light of a Markov chain. Each node in sensor networks communicates with its neighboring nodes in view of the prescribed network topology graph. The round-robin scheduling is employed to arrange the transmission order to lessen the likelihood of the occurrence of data collisions. The main goal of the work is to design a compatible distributed estimator to assure that the distributed error system is strictly (Λ1, Λ2, Λ3)y-stochastically dissipative. By applying the Lyapunov stability theory and a modified matrix decoupling way, sufficient conditions are derived by solving some convex optimization problems. An illustrative example is given to verify the validity of the provided method.

In this paper, the problem of generalized state estimation for an array of Markovian coupled networks under the round-Robin protocol (RRP) and redundant channels is investigated by using an extended dissipative property. The randomly varying coupling of the networks under consideration is governed by a Markov chain. With the aid of using the RRP, the transmission order of nodes is availably orchestrated. In this case, the probability of occurrence data collisions through a shared constrained network may be reduced. The redundant channels are also used in the signal transmission to deal with the frangibility of networks caused by a single channel in the networks. The network induced phenomena, that is, randomly occurring packet dropouts and randomly occurring quantization are fully considered. The main purpose of the research is to find a desired estimator design approach such that the extended (Ω1, Ω2, Ω3) - γ-stochastic dissipativity property of the estimation error system is guaranteed. In terms of the Lyapunov-Krasovskii methodology, the Kronecker product and an improved matrix decoupling approach, sufficient conditions for such an addressed problem are established by means of handling some convex optimization problems. Finally, the serviceability of the proposed method is explained by providing an illustrated example.