A neural network-based inversion method of a feedback linearization controller applied to a hydraulic actuator

Abstract

In this work, we use a neural network as a substitute for the traditional analytic functions employed as an inversion set in feedback linearization control algorithms applied to hydraulic actuators. Although very effective and with strong stability guarantees, feedback linearization control depends on parameters that are difficult to determine, requiring large amounts of experimental effort to be identified accurately. On the other hands, neural networks require little effort regarding parameter identification, but pose significant hindrances to the development of solid stability analyses and/or to the processing capabilities of the control hardware. Here, we combine these techniques to control the positioning of a hydraulic actuator, without requiring extensive identification procedures nor losing stability guarantees for the closed-loop system, at reasonable computing demands. The effectiveness of the proposed method is verified both theoretically and by means of experimental results.

Tracking and Analysing Error in Feedback Linearized Motion Trajectory of Hydraulic Actuator Based on the Internet of Things

“…e trajectory planning of the hydraulic actuator manipulator refers to the motion trajectory of the hydraulic actuator manipulator arm in the joint space and Cartesian space and its generation based on the kinematics and dynamics of the hydraulic actuator manipulator [2]. e trajectory planning of the hydraulic actuator manipulator is divided into joint space planning and Cartesian space trajectory planning [3].…”