Aiming at the target encirclement problem of multi-robot systems, a target hunting control method based on reinforcement learning is proposed. First, the Markov game modeling for the multi-robot system is carried out. According to the task of hunting, potential energy models are designed to meet the requirements of arriving at the desired state and avoiding obstacles. The multi-robot reinforcement learning algorithm guided by the potential energy models is presented to perform the hunting, where reinforcement learning principles are combined with the model control. Secondly, based on the potential energy models, the target-tracking hunting strategy and the target-circumnavigation hunting strategy are established. In the former, the consensus tracking of multi-robot systems is achieved by designing the velocity potential energy function. And in the latter, virtual circumnavigation points are added to construct the potential energy function, which realizes the desired circumnavigation. Finally, the effectiveness of target hunting control based on the multi-robot reinforcement learning method is verified by simulation.

The cooperation control of multiple intelligent agents (MIAs), which can solve complex engineering problems in practice, has received increasing attention. However, there are multiple disturbances in wireless sensor networks, which has a great effect on the collaboration of MIAs. In this paper, the problem of the flocking motion of second-order MIAs is addressed with collision avoidance and multiple disturbances. To estimate the matched/mismatched disturbances, the disturbance observers are designed. It is noted that the assumption that the differentials of disturbances converge to zeros in the literature is removed. A novel compound strategy is designed by using the robust auxiliary function and the potential function. The asymptotic properties of the flocking system are studied based on the Input-to-State Stability Theorem and the robust stability. Numerical simulation results verify the validity of the proposed protocol.

The flocking of multiple intelligent agents, inspired by the swarm behavior of natural phenomena, has been widely used in the engineering fields such as in unmanned aerial vehicle (UAV) and robots system. However, the performance of the system (such as response time, network throughput, and resource utilization) may be greatly affected while the intelligent agents are engaged in cooperative work. Therefore, it is concerned to accomplish the distributed cooperation while ensuring the optimal performance of the intelligent system. In this paper, we investigated the optimal control problem of distributed multiagent systems (MASs) with finite-time group flocking movement. Specifically, we propose two optimal group flocking algorithms of MASs with single-integrator model and double-integrator model. Then, we study the group consensus of distributed MASs by using modern control theory and finite-time convergence theory, where the proposed optimal control algorithms can drive MASs to achieve the group convergence in finite-time while minimizing the performance index of the intelligence system. Finally, experimental simulation shows that MASs can keep the minimum energy function under the effect of optimal control algorithm, while the intelligent agents can follow the optimal trajectory to achieve group flocking in finite time.