Iterative Learning Control of Discrete Systems with Input Backlash

Iterative learning control applies to systems that make repeated applications of the same task over a finite duration. Linear model-based designs are well-established and have seen experimental validation. In some applications, nonlinear effects can enter through the actuators, and in such cases, it may be possible to augment the design to compensate for such undesirable effects. This paper considers backlash and a new design is developed, which guarantees tracking error convergence. An example is given that demonstrates the application of the new design. Copyright (c) 2023 The Authors.