In nature, populations of organisms (e.g., wolves) exhibit a remarkable ability to coordinate their group actions, such as hunting prey or evading predators, despite the coexistence of cooperative and competitive interactions among individuals. Motivated by this intriguing phenomenon, this article investigates the cooperative consensus tracking control problem of multiagent systems (MASs) over cooperation–competition networks with asynchronous communications. That is, all followers can simultaneously achieve trajectory tracking of the leader agent, even if there exist competitive interactions between the followers and the leader. To portray the cooperation and competition level among agents, a new distance-based weight function is designed, which is more flexible than the fixed weight values in existing research works. Theoretically, the sufficient conditions for achieving consensus tracking control are obtained based on the convergence analysis method of infinite products of super-stochastic matrices. Finally, some numerical simulations are given to verify the effectiveness of the proposed consensus tracking control scheme.

Orthogonal frequency division multiplexing (OFDM) is an important technology that has been widely used in broadband wireless communications. However OFDM has some drawbacks, one of which is the high peak-to-average power ratio (PAPR) problem. Many techniques for reducing PAPR thus have been proposed in the literatures, where clipping and companding are effective techniques and have gained a lot of research interest. Based on the study of clipping and companding, this paper proposes a hybrid approach to reduce the PAPR of OFDM signals. An iterative clipping and filtering (ICF) technique and an enhanced nonlinear companding (ENC) scheme are used as the typical clipping and companding techniques in this paper. The results of the analysis show that the computational complexity of the proposed method is slightly higher than that of ENC, but much lower than that of ICF. And the simulation results show that the proposed method has better performance on PAPR reduction and bit error rate (BER) in contrast to ICF. On the other hand, compared to ENC with equal PAPR reduction, the proposed method still achieves better performance on BER.