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

2021 ◽  
Vol 43 (5) ◽  
Author(s):  
Fábio Augusto Pires Borges ◽  
Eduardo André Perondi ◽  
Mauro André Barbosa Cunha ◽  
Mario Roland Sobczyk
Keyword(s):  
Neural Network ◽  
Feedback Linearization ◽  
Inversion Method ◽  
Hydraulic Actuator

A Hybrid Feedback Linearization and Neural Network Control Algorithm Applied to a Hydraulic Actuator

2016 ◽  
Author(s):  
Fabio A. P. Borges ◽  
Eduardo André Perondi ◽  
Mauro A. B. Cunha ◽  
Mario R. Sobczyk
Keyword(s):  
Neural Network ◽  
Feedback Linearization ◽  
Network Control ◽  
Experimental Results ◽  
Hydraulic Actuator ◽  
The Neural Network ◽  
Mechanical Subsystem ◽  
Network Functions ◽  
Compensation Approach ◽  
Feedback Linearization Approach

This paper report a research investigation that proposes to replace the inversion set present in the traditional feedback linearization approach by an artificial neural network resulting in a hybrid composition approach with a neural network and an analytical term. The method is applied into a hydraulic actuator position system together with a friction compensation approach also built using neural networks. The control strategy used is based on a cascade methodology that consists of interpreting the hydraulic positioning system model as two interconnected subsystems: a mechanical subsystem driven by a hydraulic one. As experimental results have indicated a significant system behavior dependence on the oil temperature, its effects are also studied and the proposed method was improved by the inclusion of the oil temperature information as an input for the neural network functions. Experimental results show the effectiveness of the proposed controller and their advantages when compared with the traditional analytical schemes with feedback linearization approaches.

Force Control of a Hydraulic Actuator With a Neural Network Inverse Model

2021 ◽  
Vol 6 (2) ◽  
pp. 2814-2821
Author(s):  
Sung-Woo Kim ◽  
Buyoun Cho ◽  
Seunghoon Shin ◽  
Jun-Ho Oh ◽  
Jemin Hwangbo ◽  
...  
Keyword(s):  
Neural Network ◽  
Force Control ◽  
Inverse Model ◽  
Hydraulic Actuator

scholarly journals Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

2011 ◽  
Vol 8 (3) ◽  
pp. 307-323 ◽  
Author(s):  
Mohamed Bahita ◽  
Khaled Belarbi
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Feedback Linearization ◽  
Pendulum System ◽  
Rbf Network ◽  
Inverted Pendulum System ◽  
Neural Network Controller ◽  
Control Gain ◽  
On Line ◽  
Neural Feedback

In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

Sign in / Sign up

Export Citation Format

Share Document