A State Observer Based Methodology for Improving Control Schemes Employing Multiple Exogenous Feedforward Signals

Thumbnail Image



Journal Title

Journal ISSN

Volume Title


The Ohio State University

Research Projects

Organizational Units

Journal Issue


Feedback control provides the basis of many different control schemes. However, even high gain feedback may be insufficient for processes requiring high precision or non-causal behavior such as micro additive manufacturing or metrology. Exogenous feedforward inputs can be sometimes be used to provide a solution in these circumstances. These signals are carefully trained such that they produce the desired response in their target system. However, the efficacy of these signals can be greatly diminished when the systems they are applied to have different initial conditions from the ones for which the signals were designed. This problem is magnified when multiple feedforward inputs are applied sequentially. The subtype of Iterative Learning Control, Basis Task Iterative Learning Control (BTILC) involves creation of multiple exogenous feedforward signals which correspond to various learned behaviors. These signals are then applied sequentially in order to produce more complex system outputs without explicitly applying the learning algorithm to those outputs. This makes it a prime example of a control scheme which suffers from the decreased signal efficacy discussed previously. This manuscript first generates a novel algorithmic solution to these issues leveraging state information observed in the feedforward signal training process; called an Informed State Correction (ISC). Then, it presents experimental results which demonstrate a performance increase of approximately 70% in BTILC control schemes implementing an ISC. These results represent a significant increase in the efficacy of BTILC and its applicability to real-world control scenarios. Furthermore, the ISC has been posed such that it can be applied to any control scheme employing multiple exogenous feedforward signals, where it may provide similar performance benefits.



Control, Learning Control, Iterative Learning Control, Feedforward Control, Basis Task Iterative Learning Control