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This paper proposes a continuous action iterated dilemma (CAID) in evolutionary game theory with a data-driven compensation network and limited learning ability that considers both players' differences and unknown environment effects. In the traditional dynamic model of CAID, players have identical learning abilities and ignore the influence caused by the environment, which is inconsistent with real society. Therefore, we study the limited learning ability of CAID and the unknown learning mechanism caused by the environment to overcome these problems. Firstly, we propose the dynamic model of limited learning ability for CAID to reveal the law of cooperative evolution in the case when the learning abilities of players are varied. Considering the unknown learning mechanism of players, we adopt the data-driven compensation network to confront the effects of unknown dynamics caused by the environment. In addition, based on the limited learning ability and data-driven compensation network of players, the Lyapunov function is designed to prove the convergence of the CAID, avoiding the high computational complexity caused by the eigenvalues of the Jacobin matrix. In this case, simulations based on two classical dynamic model of evolutionary game theory are carried out to show the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the swarm control for large-scale omnidirectional mobile robots (OMRs) within incremental behavior is proposed to imitate the confluence behavior of animals during migration. In previous work, the number of OMRs in the swarm system was small and immutable. As such, the system lacked flexibility for swarm systems in practical applications. To solve these problems, we make several innovations. Firstly, OMRs within incremental behavior are proposed. Based on this, the incremental system of OMRs within incremental behavior is designed when the original swarm system needs assistance to form an incremental swarm system, which allows the incremental behavior happens among different swarm systems and the formation of each incremental system unchanged. Notably, incremental updating method based on second-order communication topology is proposed to update the adjacency matrix and the state matrix instead of creating a new swarm system. Then, to solve the pressure caused by the increasing number of OMRs in incremental swarm systems on calculating and storage, the incremental swarm system of large-scale OMRs based on second-order communication topology is introduced to rank the system and weaken the strong coupling relationship. In this case, the swarm control for a large-scale incremental swarm system is proposed through the backstepping method. The Lyapunov function is designed to prove the stability of the proposed controller. The simulation results verify the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]