ILC for networked nonlinear systems with random measurement losses and unknown control direction

The iterative learning control (ILC) is constructed for the discrete-time networked nonlinear systems with random measurement losses and unknown control direction. The random packet loss is modeled by an arbitrary stochastic sequence with bounded length requirement. A novel regulating approach based on truncations is introduced to make the proposed algorithm find the correct control direction adaptively. An illustrative example shows the effectiveness of the proposed approach.