With the rise of smart material actuators, it has become possible to design and build systems with a large number of small actuators. Many of these actuators exhibit a host of nonlinearities including hysteresis. Learning control algorithms can be used to guarantee good convergence of these systems even in the presence of the nonlinearities. However, they have a difficult time dealing with certain classes of noise or disturbances. We present a neighbor learning algorithm to control systems of this type with multiple identical actuators. In addition, we present a neighbor learning algorithm to control these systems for a certain class of non-identical actuators. We prove that in certain situations these algorithms provide improved convergence when compared to traditional iterative learning control techniques. Simulations results are presented that corroborate our expectations from the proofs.
Noise Reduction of Learning Control for Periodic Motion of Galvanometer Scanner