Skip to Main Content
Two basic requirements in iterative learning control (ILC), among others, are trial-to-trial trajectory invariance and system dynamics invariance. The ILC algorithm must be reinitiated if either the trajectory or system dynamics vary in-between trials. Here, we introduce a framework that flexibly applies ILC such that trajectory and system dynamics constraints are alleviated. The framework exploits the characteristic that many manufacturing operations are comprised of a set of repeated tasks, termed basis tasks here. Instead of applying ILC to the complete operation trajectory, the correct input signal to accurately perform each constitutive basis task is identified by ILC in a training routine. After basis task training, the corresponding input signals, termed basis signals, are applied in a coordinated manner based on instructions from task-oriented machine languages, such as the pervasive G-Code. This framework allows the reference trajectory to be arbitrarily chosen, provided it is comprised of the defined basis tasks. Key definitions, assumptions, and a general application with performance bounds are detailed. An example application on a micro-robotic deposition (μRD) rapid prototyping system displays the utility of the framework in fabricating two distinct structures without reinitiating the ILC algorithm in-between manufacturing operations.