Safe Air-Ground Coordination Control under Hybrid Cyberattacks via Reinforcement Learning and Self-Triggered Communication

The safe optimal coordination control problem is addressed for partially-unknown input-constrained air-ground systems under hybrid cyberattacks. Adversaries can launch denial-of-service attacks to prevent data transmission and channel manipulation attacks to tamper with interaction data. A unified distributed observer-based optimal control framework is first proposed for the heterogeneous vehicles. To achieve consensus under hybrid cyberattacks, the Zeno-free switching-type self-triggered observer is constructed based on only viable faulting neighborhood data. Then, optimal input-constrained control policies are learned via an on-policy actor-critic neural network-based learning algorithm. Sufficient conditions to guarantee the stability of the closed-loop system under the modeled hybrid cyberattacks are established. Numerical examples validate the effectiveness of the developed approach.