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In this brief, the problem of tracking control for uncertain nonlinear systems with periodic disturbances is studied. Based on the prescribed performance technology, two kinds of error transformation functions different from the traditional backstepping method are introduced, such that the proposed low-computation adaptive control method can ensure the boundedness of the signals of the closed-loop system. Meanwhile, the counter evidence method reveals that the system output can achieve accurate tracking. Compared with existing results, firstly, the complexity explosion problem is avoided without introducing a filter, making our result more applicable and less complex. Secondly, for the time-dependent periodic disturbances considered in the system, a function approximator based on Fourier series expansion can achieve accurate approximation with the least number of Gaussian basis functions. Comparative simulation results of the van der Pol oscillator illustrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

In proof-of-work (PoW)-based blockchain networks, the miners contribute their distributed computation in solving a crypto-puzzle competition to win the reward. To secure stable profits, some miners organize mining pools and share the rewards from the pool in proportion to each miner’s contribution. However, some miners may exhibit malicious behaviors which cause a waste of distributed computation resource, even posing a threat on the efficiency of blockchain networks. In this paper, we propose a new game-theoretic framework to incentivize miners mining honestly and help to bring about a higher total welfare of blockchain networks. We first formulate the mining process as a noncooperative iterated game. We then propose a mechanism in terms of zero-determinant strategies (ZD strategies) to encourage the cooperative mining and improve the efficiency of mining in PoW-based blockchain networks. In addition, we theoretically analyze the maximum system welfare of the target pool through the method of optimization. Numerical illustrations are also presented to support our theoretical results. [ABSTRACT FROM AUTHOR]

With ever increasing people's awareness of low carbon and environmental protection, electric vehicles are gradually gaining wide popularity. However, the driving endurance of the electric vehicle is the biggest shortage that hinders the fully acceptance of this new vehicle technology. To deal with this shortage, this paper proposed a vehicle-to-vehicle (V2V) electricity trading scheme based on Bayesian game pricing in blockchain-enabled Internet of vehicles (BIoV). Specifically, the Bayesian game is adopted for pricing in the distributed BIoV with incomplete information sharing. The optimal pricing under the linear strategic equilibrium has been obtained which maximizes the utilities of both sides of electricity transaction. The transaction volume is determined from the formulated convex problem that maximizes the social welfare. Then, the pricing game is implemented by the dedicated smart contract. Blockchain guarantees its trustworthiness, security, and reliability. Finally, the experimental results show that referring to the benchmark of static game with complete information, the proposed Bayesian game with incomplete information can achieve approximate satisfaction of users. The degree of approximation can reach to 98% when the pricing ranges of buyers and sellers are close. Moreover, the proposed scheme has great advantages over the static game with complete information in terms of communication overhead and timeliness in the decentralized IoVs. [ABSTRACT FROM AUTHOR]