Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system

Jimoh Olarewaju Pedro; Olurotimi Akintunde Dahunsi

International Journal of Applied Mathematics and Computer Science (2011)

  • Volume: 21, Issue: 1, page 137-147
  • ISSN: 1641-876X

Abstract

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This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-offreedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system's ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is developed using input-output data sets obtained from mathematical model simulation. The NN model is trained using the Levenberg-Marquardt optimization algorithm. The proposed controller is compared with a constant-gain PID controller (based on the Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road disturbance. Simulation results demonstrate the superior performance of the proposed direct adaptive NNFBL controller over the generic PID controller in rejecting the deterministic road disturbance. This superior performance is achieved at a much lower control cost within the stipulated constraints.

How to cite

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Jimoh Olarewaju Pedro, and Olurotimi Akintunde Dahunsi. "Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system." International Journal of Applied Mathematics and Computer Science 21.1 (2011): 137-147. <http://eudml.org/doc/208029>.

@article{JimohOlarewajuPedro2011,
abstract = {This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-offreedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system's ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is developed using input-output data sets obtained from mathematical model simulation. The NN model is trained using the Levenberg-Marquardt optimization algorithm. The proposed controller is compared with a constant-gain PID controller (based on the Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road disturbance. Simulation results demonstrate the superior performance of the proposed direct adaptive NNFBL controller over the generic PID controller in rejecting the deterministic road disturbance. This superior performance is achieved at a much lower control cost within the stipulated constraints.},
author = {Jimoh Olarewaju Pedro, Olurotimi Akintunde Dahunsi},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {neural networks; direct adaptive control; feedback linearization control; PID control; ride comfort; suspension system; servo-hydraulics},
language = {eng},
number = {1},
pages = {137-147},
title = {Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system},
url = {http://eudml.org/doc/208029},
volume = {21},
year = {2011},
}

TY - JOUR
AU - Jimoh Olarewaju Pedro
AU - Olurotimi Akintunde Dahunsi
TI - Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system
JO - International Journal of Applied Mathematics and Computer Science
PY - 2011
VL - 21
IS - 1
SP - 137
EP - 147
AB - This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-offreedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system's ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is developed using input-output data sets obtained from mathematical model simulation. The NN model is trained using the Levenberg-Marquardt optimization algorithm. The proposed controller is compared with a constant-gain PID controller (based on the Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road disturbance. Simulation results demonstrate the superior performance of the proposed direct adaptive NNFBL controller over the generic PID controller in rejecting the deterministic road disturbance. This superior performance is achieved at a much lower control cost within the stipulated constraints.
LA - eng
KW - neural networks; direct adaptive control; feedback linearization control; PID control; ride comfort; suspension system; servo-hydraulics
UR - http://eudml.org/doc/208029
ER -

References

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  3. Stanisław Bańka, Paweł Dworak, Krzysztof Jaroszewski, Design of a multivariable neural controller for control of a nonlinear MIMO plant
  4. Łukasz Bartczuk, Andrzej Przybył, Krzysztof Cpałka, A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

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