Control of a semi-active suspension with a magnetorheological damper modeled via Takagi-Sugeno

Félix-Herrán, Luis C.; Mehdi, Driss; Soto, Rogelio; Rodríguez-Ortiz, J. de J.; Ramírez-Mendoza, Ricardo A.

doi:10.1109/med.2010.5547876

Cited by 8 publications

(13 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work herein goes beyond the outcomes in [25]. Moreover, it has the potential to be useful for [13][14][15][16], where no actuator dynamics are considered.…”

Section: Discussionmentioning

confidence: 99%

“…Following the approach in [28], a Takagi-Sugeno fuzzy model [31] needs to be obtained in order to synthesize a controller. The first step is to rearrange the equations of motion in such a way that nonlinearities can be clearly identified [25] and, if possible, to group them into one single nonlinearity. It is important to keep the number of nonlinearities as small as possible because the resulting T-S system is going to have 2 (#nonlinearities) linear subsystems [31]; therefore, present research considers the outcomes in Félix-Herrán et al [25], in order to obtain a T-S model with fewer subsystems by considering Bouc-Wen approach instead of the Spencer model [6].…”

Section: Takagi-sugeno Fuzzymentioning

confidence: 99%

“…From these recent outcomes and previous work, the objective of this research is to develop a model that includes actuator's nonlinear dynamics following the T-S fuzzy approach. This combination is the herein contribution (taking the work in Félix-Herrán et al [25] as the baseline).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Takagi-Sugeno Fuzzy Model of a One-Half Semiactive Vehicle Suspension: Lateral Approach

Félix-Herrán

Mehdi

Rodríguez-Ortiz

et al. 2015

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

This work presents a novel semiactive model of a one-half lateral vehicle suspension. The contribution of this research is the inclusion of actuator dynamics (two magnetorheological nonlinear dampers) in the modelling, which means that more realistic outcomes will be obtained, because, in real life, actuators have physical limitations. Takagi-Sugeno (T-S) fuzzy approach is applied to a four-degree-of-freedom (4-DOF) lateral one-half vehicle suspension. The system has two magnetorheological (MR) dampers, whose numerical values come from a real characterization. T-S allows handling suspension’s components and actuator’s nonlinearities (hysteresis, saturation, and viscoplasticity) by means of a set of linear subsystems interconnected via fuzzy membership functions. Due to their linearity, each subsystem can be handled with the very well-known control theory, for example, stability and performance indexes (this is an advantage of the T-S approach). To the best of authors’ knowledge, reported work does not include the aforementioned nonlinearities in the modelling. The generated model is validated via a case of study with simulation results. This research is paramount because it introduces a more accurate (the actuator dynamics, a complex nonlinear subsystem) model that could be applied to one-half vehicle suspension control purposes. Suspension systems are extremely important for passenger comfort and stability in ground vehicles.

show abstract

“…The work herein goes beyond the outcomes in [25]. Moreover, it has the potential to be useful for [13][14][15][16], where no actuator dynamics are considered.…”

Section: Discussionmentioning

confidence: 99%

Section: Takagi-sugeno Fuzzymentioning

confidence: 99%

See 1 more Smart Citation

Takagi-Sugeno Fuzzy Model of a One-Half Semiactive Vehicle Suspension: Lateral Approach

Félix-Herrán

Mehdi

Rodríguez-Ortiz

et al. 2015

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, a Takagi-Sugeno (TS) fuzzy model-based control strategy [20,21] will be proposed such that the nonlinear dynamic characteristics of MR dampers can be considered in the control system design process and the desired control performance can be fully realized for the practical system when the actual MR dampers are installed. Different from the study of [22], where the TS fuzzy model of an MR damper was identified by training the input-output data sets and the accuracy was fully dependent on expert experience in selecting appropriate fuzzy sets and fuzzy rules [23], the TS fuzzy model of an MR damper will be obtained in this paper by considering the Bouc-Wen MR damper model [24] and using the approach of 'sector nonlinearity' [25]. In addition, taking into consideration the vehicle suspension performance requirements on ride comfort, road holding, and suspension deflection, and the constraint on the input voltage to the MR damper, the design of an H ∞ controller is proposed so that both the closed-loop system stability and the closed-loop performance can be theoretically guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Direct voltage control of magnetorheological damper for vehicle suspensions

Lam

Cheung

et al. 2013

Smart Mater. Struct.

View full text Add to dashboard Cite

The paper presents a study on the direct voltage control of a magnetorheological (MR) damper for application in vehicle suspensions. As MR damper dynamics is highly nonlinear, the direct control system design for an MR damper is difficult. Representing an MR damper by a Takagi–Sugeno (TS) fuzzy model enables the linear control theory to be directly applied to design the MR damper controller. In this paper, first the MR damper dynamics is represented by a TS fuzzy model, and then an H∞ controller that considers the suspension performance requirements and the constraint on the input voltage for the MR damper is designed. Furthermore, considering the case that not all the state variables are measurable in practice, the design of an H∞ observer with immeasurable premise variables and the design of a robust controller are proposed, respectively. Numerical simulations are used to validate the effectiveness of the proposed approaches.

show abstract

“…This controller had good performance in the vehicle's ride comfort and the road handling. Felix-Herran, et al [6] also presented a Takagi-Sugeno (T-S) Fuzzy model for a two-degrees-of freedom (2-DOF) one-quarter-vehicle semi-active suspension with an MR damper. The controller gain is applied via Parallel Distributed Compensation (PDC) through a static state feedback controller for each linear subsystem.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Adaptive Neuro Fuzzy Inference System controller on magneto rheological damper suspension

Nugroho

et al. 2013

2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

View full text Add to dashboard Cite

In this paper, we consider the implementation of an Adaptive Neuro Fuzzy Inference System (ANFIS) technique to control a quarter vehicle suspension systems with a semi active magneto rheological (MR) damper. A quarter-car suspension model together with the MR damper is set up, and a semi-active controller composed of the Skyhook-Groundhook controller and the ANFIS model is designed. The design of ANFIS method use Fuzzy model with Gaussian membership function configuration and back propagation method as learning Fuzzy's parameter. This control strategy is proposed to compensate the uncertainties problem of MR damper. Finally, in the simulation of the comparison of semi active suspension ANFIS controller, Skyhook-Groundhook controller and PassiveSuspension is shown using MATLAB. The simulation results demonstrate that the ANFIS method provides better isolation than that via Passive Suspension.

show abstract

Control of a semi-active suspension with a magnetorheological damper modeled via Takagi-Sugeno

Cited by 8 publications

References 17 publications

Takagi-Sugeno Fuzzy Model of a One-Half Semiactive Vehicle Suspension: Lateral Approach

Takagi-Sugeno Fuzzy Model of a One-Half Semiactive Vehicle Suspension: Lateral Approach

Direct voltage control of magnetorheological damper for vehicle suspensions

Implementation of Adaptive Neuro Fuzzy Inference System controller on magneto rheological damper suspension

Contact Info

Product

Resources

About