Iterative Learning Control of Multi-Agent Systems under Changing Reference Trajectory*

Abstract

In modern smart manufacturing, robots are often connected via a network, and their task can change according to a predetermined program. Iterative learning control (ILC) is widely used for robots executing high-precision operations. Under network conditions, the efficiency of ILC algorithms may decrease if the program is restructured. In particular, the tracking error may temporarily increase to an unacceptable value when changing the reference trajectory. This paper considers a multi-agent model of such a network: the reference trajectory and parameters change between passes according to a known program, the plant is subjected to random disturbances, and measurements are carried out with noise. A distributed ILC design method is proposed based on the vector Lyapunov function method recently developed for repetitive processes in combination with Kalman filtering. This design method ensures the convergence of the tracking error and reduces its increase caused by a change in the reference trajectory. An illustrative example is given to confirm the effectiveness of the proposed method.