In practice, the dual constraints of limited interaction capabilities and system uncertainties make it difficult for large-scale multiagent systems (MASs) to achieve intelligent collaboration with incomplete local relative information. In this article, a review is conducted on the recent development of MASs intended for intelligent control, including consensus problem, formation control, and flocking control. Based on the limitations of the interaction level and the constraints of the individual system level, the published results on intelligent control are categorized into limited sensing-based control, event-based control, pinning-based control, resilient control, and collaborative control under system constraints. Also, the applications of intelligent control for MASs are presented, especially for robotics, complex networks, and transportation. Finally, a discussion is given about the challenges and future directions of research in this field.

In this article, a distributed adaptive event-triggered formation control strategy is proposed for unified nonlinear heterogeneous multiagent systems under uncertainties and disturbances to achieve time-varying formations. To reduce the frequency of data transmission, a distributed dual adaptive observer with an event-triggered strategy is developed to estimate the states of a reference exosystem. Without incurring prior global information about a communication graph, a novel robust formation controller, with dynamic distributed compensators for uncertainties and disturbances, is designed based on an observer result and a nonlinear internal control principle. Finally, both simulation and experiment are conducted for tracking and patrolling formation to verify the effectiveness of the proposed formation control strategy and its robustness. Note to Practitioners—This article addresses the collaborative formation problem of multiagent systems that has potential applications in transportation and disaster relief. The design of robust and energy-saving coordination strategies is challenging in heterogeneous multivehicle systems. The proposed distributed method is suitable for large-scale uncertain heterogeneous systems, and the use of the dual adaptive event-triggered strategy reduces the data transmission rate.

A safe time-varying formation (TVF) control framework is proposed in this article for heterogeneous multiagent systems under the constraints of denial of service (DoS) attacks, noncooperative dynamic obstacles, and input saturation. The framework integrates both the cyber-layer and physical-layer components to address the challenges posed by these adverse conditions. In the cyber-layer, a distributed resilient observer is provided based on a control Lyapunov function (CLF)-quadratic program (QP). This observer estimates a reference exosystem, effectively decoupling heterogeneous dynamics from unsafe networks and optimizing the system resilience against DoS attacks. At the physical-layer, for the first time, a collision-free TVF controller is presented based on the CLF-exponential control barrier function-QP. The controller guarantees high-order heterogeneous agents’ operation safety under noncooperative obstacles and input saturation. The effectiveness and advantages of the proposed algorithms are verified through the comparative simulations and experiments conducted on a physical system comprising unmanned aerial vehicles and unmanned ground vehicles.

This article presents a novel learning-based collaborative control framework to ensure communication security and formation safety of nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks, model uncertainties, and barriers in environments. The framework has a distributed and decoupled design at the cyber-layer and the physical layer. A resilient control Lyapunov function-quadratic programming (RCLF-QP)-based observer is first proposed to achieve secure reference state estimation under DoS attacks at the cyber-layer. Based on deep reinforcement learning (RL) and control barrier function (CBF), a safety-critical formation controller is designed at the physical layer to ensure safe collaborations between uncertain agents in dynamic environments. The framework is applied to autonomous vehicles for area scanning formations with barriers in environments. The comparative experimental results demonstrate that the proposed framework can effectively improve the resilience and robustness of the system.

This article presents a novel event and learning-based resilient formation control strategy for heterogeneous multiagent systems subjected to denial-of-service (DoS) attacks and uncertainties. It involves a decoupled cyber-layer and physical system layer design that enables a distributed and model-free approach. In the cyber-layer, the design is an event-triggered resilient observer for a reference exosystem estimation under DoS attacks using dual adaptive laws and an optimal algorithm. This approach eliminates the need for global information of the communication topology and enhances system resilience under attacks. In the physical system layer, the design is a model-free formation output controller for heterogeneous agents based on off-policy reinforcement learning. The incorporation of a new rank condition improves the convergence performance. Experiments using unmanned ground vehicles are conducted for scanning a physical area to verify the effectiveness and resilience of the proposed control strategy.

In this article, we design an adaptive leader–follower consensus controller for a class of nonlinear multiagent systems in the presence of time-varying asymmetric output constraints and unknown control directions. A new state transformation approach is introduced for each agent to transform the output into an equivalent unconstrained state. An adaptive neural network-based backstepping control method and a Nussbaum function approach are integrated to design the leader–follower consensus controller that compensates for the unknown control directions and guarantees that the consensus tracking error converges to a small compact set. Examples are given to demonstrate the effectiveness of the proposed new design techniques.