Magneto-rheological dampers - Model influence on the semi-active suspension performance

Juan Carlos Tudón Martínez, Diana Hernandez-Alcantara, Luis Antonio Amezquita-Brooks, Ruben Morales-Menendez, Jorge Lozoya-Santos, Osvaldo Aquines Gutiérrez

Research output: Contribution to journalArticle

Abstract

Recently, automotive industry has adopted semi-active damper systems to improve handling and comfort properties of vehicles. Nowadays, Magneto-Rheological (MR) dampers are among the most effective solutions; with the control algorithm used for their operation being a key element. While basic controllers do not require mathematical damper models, improved performance can be achieved if these are available. Usually, the accuracy of a particular set of models can be assessed by evaluating standard quantitative metrics. However, two models with similar error-metrics can still have widely different qualitative properties. In this context, the main aim of this paper is to study the effects that may appear in the closed-loop performance of an automotive suspension system when the damper model is unable to represent crucial nonlinear MR phenomena. To highlight the model influence on the controller synthesis and subsequently on the suspension performance, two damper models with different accuracy levels were chosen: an Artificial Neural Networks (ANN)-based model is compared with the classical Bingham model. First, their accuracy is experimentally validated using typical error-metrics. Afterwards, the same suspension control strategy is designed using both models. Frequency-Estimation-Based control was selected because it better exploits available model data than other typical strategies such as sky-hook. The resulting performance is assessed with a software-in-the-loop approach using CarSim ® and complemented with a hardware-in-the-loop implementation using a CAN-bus, both closed-loop control cases use a Simulation-Oriented ANN model as benchmark to represent the MR damper nonlinearities. Results show that although the difference in error-metrics between models can be small using typical identification methods (e.g. 16% in one scenario), suspension performance in comfort and road-holding are significantly different. Error-metrics can be deceptive for assessing the effectiveness of MR damper models during the controller design phase. Accurate qualitative modeling in the pre/post-yield regions are the main factors which determine the resulting controller performance.

Original languageEnglish
Article number105030
JournalSmart Materials and Structures
Volume28
Issue number10
DOIs
Publication statusPublished - 5 Sep 2019

Fingerprint

dampers
Suspensions
controllers
Controllers
comfort
Neural networks
Frequency estimation
Hooks
hooks
Automotive industry
roads
Identification (control systems)
sky
vehicles
hardware
Mathematical models
industries
nonlinearity

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Civil and Structural Engineering
  • Atomic and Molecular Physics, and Optics
  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Electrical and Electronic Engineering

Cite this

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title = "Magneto-rheological dampers - Model influence on the semi-active suspension performance",
abstract = "Recently, automotive industry has adopted semi-active damper systems to improve handling and comfort properties of vehicles. Nowadays, Magneto-Rheological (MR) dampers are among the most effective solutions; with the control algorithm used for their operation being a key element. While basic controllers do not require mathematical damper models, improved performance can be achieved if these are available. Usually, the accuracy of a particular set of models can be assessed by evaluating standard quantitative metrics. However, two models with similar error-metrics can still have widely different qualitative properties. In this context, the main aim of this paper is to study the effects that may appear in the closed-loop performance of an automotive suspension system when the damper model is unable to represent crucial nonlinear MR phenomena. To highlight the model influence on the controller synthesis and subsequently on the suspension performance, two damper models with different accuracy levels were chosen: an Artificial Neural Networks (ANN)-based model is compared with the classical Bingham model. First, their accuracy is experimentally validated using typical error-metrics. Afterwards, the same suspension control strategy is designed using both models. Frequency-Estimation-Based control was selected because it better exploits available model data than other typical strategies such as sky-hook. The resulting performance is assessed with a software-in-the-loop approach using CarSim {\circledR} and complemented with a hardware-in-the-loop implementation using a CAN-bus, both closed-loop control cases use a Simulation-Oriented ANN model as benchmark to represent the MR damper nonlinearities. Results show that although the difference in error-metrics between models can be small using typical identification methods (e.g. 16{\%} in one scenario), suspension performance in comfort and road-holding are significantly different. Error-metrics can be deceptive for assessing the effectiveness of MR damper models during the controller design phase. Accurate qualitative modeling in the pre/post-yield regions are the main factors which determine the resulting controller performance.",
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Magneto-rheological dampers - Model influence on the semi-active suspension performance. / Tudón Martínez, Juan Carlos; Hernandez-Alcantara, Diana; Amezquita-Brooks, Luis Antonio; Morales-Menendez, Ruben; Lozoya-Santos, Jorge; Aquines Gutiérrez, Osvaldo.

In: Smart Materials and Structures, Vol. 28, No. 10, 105030, 05.09.2019.

Research output: Contribution to journalArticle

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