Unmanned Cluster Control Resilience Under Network Attacks

The patent applies preliminary action by constructing uncertainty boundary functions before the actual control process. The method pre-establishes the bounds of system uncertainties and network attack influences, allowing the controller to prepare compensation strategies in advance. This is reflected in Step S4 where uncertainty boundary functions are constructed based on dynamic models, enabling the system to anticipate and counteract potential disturbances before they compromise stability.

The patent implements feedback through adaptive robust controllers that continuously monitor and adjust control inputs based on system state and uncertainty estimates. The controller uses real-time feedback from the uncertainty boundary functions and system performance to dynamically compensate for network attacks and parameter variations. This is evident in Step S5 where adaptive robust controllers are designed to actively respond to changing conditions while maintaining stability.

The patent applies segmentation by dividing the complex control problem into distinct modules: dynamic model construction, uncertainty boundary function construction, and adaptive robust controller design. Each module handles a specific aspect of the control challenge, making the overall complex system more manageable. The uncertainty boundary function separates the estimation of uncertainty bounds from the actual control law, allowing independent optimization of each component.

The patent utilizes parameter changes by adapting controller parameters based on the constructed uncertainty boundary functions. The adaptive robust controller dynamically adjusts its parameters according to the estimated uncertainty levels and network attack conditions. This allows the system to maintain high reliability under varying attack scenarios while managing complexity through parameter adaptation rather than structural redesign.