Data-driven fault-tolerant consensus control for constrained nonlinear multiagent systems via adaptive dynamic programming

In this paper, a data-driven fault-tolerant control (FTC) method is proposed to solve the consensus problem of constrained multiagent systems (MASs) with denial-of-service attacks. First, a resilient distributed observer is introduced to extract the leader's state in real-time for each follower, even in the presence of attacks. A nonlinear mapping is employed to transform the original system with state constraints into an equivalent constraint-free system, ensuring that the original system's states remain within prescribed limits. Then, an adaptive dynamic programming (ADP)-based FTC scheme is designed for the system to mitigate the effects of actuator faults, enabling the nominal system to balance cost and performance. The ADP algorithm is implemented using an actor-critic structure to solve the Hamilton-Jacobi-Bellman equation based on system data collected via the least-squares method. In this framework, the designed controller is data-driven rather than reliant on precise system information, which broadens the controller's applicability to systems with unknown dynamics. Finally, the effectiveness of the established controller is validated through two examples. • The fault-tolerant controller ensures consensus under faults and attacks. • A resilient observer guarantees real-time leader state estimation under attacks. • A nonlinear mapping enforces state constraints while preserving system dynamics. • The data-driven method achieves control without requiring system models.