Robust networked iterative learning control of a variable configurationsystem under random disturbances

The paper considers the iterative learning control design problem of a system, consisting of identical subsystems (agents) under changing the information network configuration. The agents represent discrete linear dynamic plants with affine model of uncertainty operating in a repetitive mode. Each plant is under the influence of random disturbances, and the output variables of the plants are measured in the presence of noises. The configurations of the information network determine the group of functioning agents and the type of information exchange between agents. The configuration change takes place in accordance with certain external rules. The control design is based on the divergent method of the vector Lyapunov function. The results of modeling the obtained control law for a group of manipulators with flexible single-links are presented.