Online Learning Control of Hydraulic Excavators Based on Echo-State Networks
In some of recent advances in automation of construction equipment, much research has been conducted on the control of hydraulic excavators in both industry and academia for the benefit of safety and efficiency. However, most relevant works have employed model-based control approaches that require a mathematical representation of the target plant. For hydraulic excavators, obtaining a useful dynamic model for control can be challenging due to the nonlinearity of the hydraulic servo system. With this in mind, this paper investigates the feasibility of an online learning control framework based on echo-state networks (ESNs) to the position control of hydraulic excavators. While ESNs are a class of recurrent neural networks, the training of ESNs corresponds to solving a linear regression problem, thus making it suitable for online implementation. By exploiting the dynamic properties of ESNs, the deployed control framework uses the input and output signals of the plant to learn an inverse model, which is then used to simultaneously generate control inputs to track the desired trajectory. Experiments conducted on a 21-ton class hydraulic excavator show the promising results in that accurate tracking is achieved even in the absence of a dynamic model.