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This paper proposes a novel adaptive dynamic programming (ADP) approach to address the optimal consensus control problem for discrete-time multiagent systems (MASs). Compared with the traditional optimal control algorithms for MASs, the proposed algorithm is designed on the basis of the event-triggered scheme which can save the communication and computation resources. First, the consensus tracking problem is transferred into the input-state stable (ISS) problem. Based on this, the event-triggered condition for each agent is designed and the event-triggered ADP is presented. Second, neural networks are introduced to simplify the application of the proposed algorithm. Third, the stability analysis of the MASs under the event-triggered conditions is provided and the estimate errors of the neural networks’ weights are also proved to be ultimately uniformly bounded. Finally, the simulation results demonstrate the effectiveness of the event-triggered ADP consensus control method.

Routing protocols based on gradient and cluster in wireless sensor network is analyzed. A concept of sensor node's potential is proposed. By adopting the technologies of building potential, clustering based on potential, the adjustive power routing of cluster head, a new potential-based routing protocol (PBRP) is designed. Simulation results show that the new routing protocol provides longer lifetime and higher reliability.