Designing Input Shapers Using Reinforcement Learning

Abstract

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Over the last few decades, an open-loop control method known as input shaping has shown to be effective in minimizing unwanted residual vibration of flexible machines. Traditionally, an input shaper is created through either a direct solution of a set of equations describing the system response or through an optimization problem addressing those equations. This paper introduces a new way to design an input shaper using a machine learning approach. It also proposes some changes to a traditional machine learning algorithm as an alternative solution for one of the most fundamental problems in reinforcement learning known as the exploration-exploitation dilemma. This paper serves as a foundation for a new class of input shapers designed by machine learning that could potentially be developed into a wide-ranging adaptive control solution.