This article investigates a fully data-driven method to solve the robust output formation tracking control problem for the multiagent system (MAS) under actuator faults. The outputs of the followers are controlled to track those of multiple leaders with respect to a convex point while achieving an expected time-varying formation. To obviate the requirement of various system prior knowledge in typical MAS control, a hierarchical frame is developed with three learning and control stages using the online measured data. First, a distributed adaptive observer is designed to coordinate the state convex of multiple leaders while estimating unknown dynamics. The adaptive mechanism relaxes the demand for global topology. Second, by collecting and reusing the online system data, an off-policy reinforcement learning (RL) method is proposed in a continuous form to acquire nominal feedback gains from partial observations of the followers. Essential system models are learned along with the RL process, while solutions to the output regulation equations are implicitly obtained. Third, a comprehensive robust controller is further presented based on the previous learning results. To address the actuator faults with efficiency loss and bias, the adaptive neural networks and robust compensations are utilized in a model-free manner. The output formation tracking is achieved under a derived feasibility condition while stabilities of the learning and control methods are analyzed. Finally, simulation results demonstrate the validity of this fully data-driven control frame.

This paper investigates the formation tracking control problem for heterogeneous air-ground systems with quadrotor unmanned aerial vehicles (UAVs) and wheeled unmanned ground vehicles (UGVs) of nonlinear dynamics. A hierarchical learning-based framework is proposed to address the unknown tracking leader and multiple faults. First, an adaptive observer is designed to estimate both the dynamics and state of the tracking leader via a directed network. Second, based on the data from an internal model filter and system measurements, an off-policy reinforcement learning (RL) algorithm is applied to learn a unified tracking planner for both UAVs and UGVs. The planner is coordinated with time-varying leader and formation in the RL process, while internal dynamics are learned. Third, a finite-time differential filter and a virtual reference are respectively constructed for UAVs and UGVs, and transform the planner's outputs into tracking commands. By integrating adaptive neural networks and robust compensations with backstepping designs, the fault-tolerant formation tracking is realized. Finally, the learning and control framework on air-ground systems are validated by numerical simulations.

This paper investigates a resilient output formation-containment tracking (FCT) problem for heterogeneous multi-agent systems (MASs) under unknown dynamics and uncertainties. A learning-based control framework using online dynamic data is proposed with three hierarchical phases. First, fully distributed observers for agents with various types of objectives are presented under a directed graph. The estimations of tracking reference and time-varying formation are coordinated in terms of both dynamics and states. Second, dynamic data filters based on the internal model principle and partial observations are introduced to reconstruct the MASs information and formulate a virtual tracking system, where the reinforcement learning (RL) technique is applied. Based on two proposed off-policy schemes, the RL algorithm is adapted to a hybrid form under the dynamic data. An ideal tracking controller is uniformly learned and essential dynamics are extracted from the same data. Third, the integrated resilient output FCT controller is further derived using previous learning results. The adaptive neural networks and compensation functions are utilized in a data-driven manner to address unknown faults and uncertainties. The integration of filtering, estimation, and learning broadens a more general control framework than existing results. Finally, validations are demonstrated by numerical simulations.

This paper studies the multi-agent differential game based problem and its application to cooperative synchronization control. A systematized formulation a