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This paper is concerned with data-driven distributed optimal consensus control for unknown multiagent systems (MASs) with input delays. The input-delayed MAS model is first converted into a delay-free form using a model reduction method. By establishing an equivalent relationship on the predesigned performance indices of the two MASs, optimal consensus control of input-delayed MAS can be fully transformed to that of delay-free MAS. Based on the coupled Hamilton-Jacobi equations and Bellman's optimality principle, optimal consensus control policies are derived for the transformed delay-free MAS. Then a policy iteration algorithm based on distributed asynchronous update mechanism is proposed to learn the coupled Hamilton-Jacobi-Bellman equations online. To perform the proposed data-driven adaptive dynamic programming algorithm, we adopt the measured data-based critic-actor neural networks to approximate the value functions and the control policies, respectively. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.

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Summary This paper investigates the consensus problem for discrete‐time networked multiagent systems, where the information is exchanged through a shared network with communication delays. Based on the event‐triggered communication scheme together with the networked predictive control scheme, a novel distributed adaptive model‐based event‐triggered predictive control protocol is proposed to reduce the network communication burden and compensate for the communication delays actively. By using the virtual interval segmentation technique and the time‐delay modeling method together with defining a set of switching signals, the closed‐loop systems are established in forms of switching time‐delay systems. The problem of simultaneously deriving the controller gain matrix and the event‐triggering parameter matrices is cast into standard linear matrix inequalities problem. A convincing simulation example is given to demonstrate the theoretical results.

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