In this letter, we focus on the flocking control and gait synchronization control of multiple quadruped robots to achieve the movement during patrol tasks. To achieve these goals, we propose an optimization-based distributed flocking controller and a model predictive control (MPC)-based gait synchronization controller. A constrained omnidirectional motion kinematic model is employed to simplify the model of a single quadruped robot. In the flocking controller, position and orientation consistency among the quadruped robots are considered. The constraint is designed to minimize the lateral movement velocity of the quadruped robots. As each robot approaches the destination, it maintains alignment of its orientation with the destination. Additionally, a control barrier function (CBF) is designed to maintain a safe distance between the quadruped robots. Moreover, the proposed MPC-based gait synchronization controller effectively achieves gait synchronization among multiple quadruped robots. Simulation and real world experiments demonstrate the effectiveness of the proposed algorithm.

The secure consensus problem of multiagent systems with input saturation and denial-of-service (DoS) attacks constraints is an interesting research problem. To solve this problem, the assumptions that all the agents own the same saturation level and suffer the same DoS attacks from a single adversary are usually made. This brief removes the above assumptions and investigates the secure consensus problem of multiagent systems with different saturation levels and multiple DoS attacks. The studied multiagent systems have different layers, in which different saturation levels are studied for different layers. Moreover, the DoS attacks are launched from different adversaries and will cause different effects for different agents. To achieve the desired objective, two different consensus protocols for agents in different levels are first designed using the low-gain technique. Then, the investigated dynamic system under DoS attacks is modeled by a switched system by categorizing the DoS attacks into several types. The controller is proposed by solving parametric algebraic Riccati equation (ARE). Sufficient conditions for the DoS attack duration on each channel are derived.

In many existing multi-agent reinforcement learning tasks, each agent observes all the other agents from its own perspective. In addition, the training process is centralized, namely the critic of each agent can access the policies of all the agents. This scheme has certain limitations since every single agent can only obtain the information of its neighbor agents due to the communication range in practical applications. Therefore, in this paper, a multi-agent distributed deep deterministic policy gradient (MAD3PG) approach is presented with decentralized actors and distributed critics to realize multi-agent distributed tracking. The distinguishing feature of the proposed framework is that we adopted the multi-agent distributed training with decentralized execution, where each critic only takes the agent’s and the neighbor agents’ policies into account. Experiments were conducted in the distributed tracking tasks based on multi-agent particle environments where $N(N=3,N=5)$ agents track a target agent with partial observation. The results showed that the proposed method achieves a higher reward with a shorter training time compared to other methods, including MADDPG, DDPG, PPO, and DQN. The proposed novel method leads to a more efficient and effective multi-agent tracking.

This paper studies the switching topologies and tracking agent replacements for a class of nonlinear multi-agent systems with external disturbances. In particular, it is assumed that a number of monitoring agents are randomly deployed in a two-dimensional space. The agents are committed to tracking any intrusion targets in this area. When one of the following agents exits tracking for some reasons, the agent closer to the target joins the tracking team to ensure the number of tracking agents does not change. In order to achieve the successful tracking of the target, this paper designs a sliding mode controller for the relay multi-agent system with tracking agent replacements. Furthermore, an adaptive law is proposed to estimate the upper bound of disturbance in the relay tracking process. Next, the stability of the overall system will be analysed by applying the multiple Lyapunov function method based on the average dwell time. Finally, the effectiveness of the tracking strategy is verified by numerical simulations.

This brief investigates the nonsingular terminal sliding model control for a class of multi-agent tracking problems. To eliminate the singularity problem that naturally arises in the design of sliding mode control, a novel continuous function is proposed and applied in the controller. By using the modified controller, the tracking errors can be steered to zero in finite time. Strict stability analysis is presented via the Lyapunov theory, and the convergence time is obtained. Moreover, the finite-time stability and convergence time for multi-agent tracking systems with replacement of agents and error jumps are also investigated. Finally, the novel nonsingular terminal sliding mode control scheme is utilized to a jerk circuit in order to verify its effectiveness as well as its control performance.