Security data-driven iterative learning control for unknown nonlinear systems with hybrid attacks and fading measurements.

To achieve the stabilization objective of a class of nonlinear systems with unknown dynamics, this paper studies the security data-driven control problem under iterative learning schemes, where the faded channels are suffering from randomly hybrid attacks. The networked attacks try to obstruct the data transmission by injecting the false data. The plant is transformed into a dynamic data-model with the iteration-related linearization method. Then, two data-driven control methods, including a compensation scheme multiplied by increasing gains, are designed by using incomplete I/O signals. The effectiveness of the algorithms and the influence brought by stochastic issues are analyzed theoretically. Finally, a numerical simulation and a tracking example of agricultural vehicles illustrate the validity of the design.