Design of compensatory backstepping controller for nonlinear magnetorheological dampers

Gao, Zhendong; Wong, Pak Kin; Zhao, Jing; Hua, Xingqi; Ma, Xinbo; Xie, Zhengchao

doi:10.1016/j.apm.2022.10.001

Cited by 10 publications

(6 citation statements)

References 28 publications

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“…The unknown variables, denoted as c, D 1 , D 2 , D 3 , ∆P (t), z 1 and z 2 can be determined by combining the equations ( 8) and (10), and the theoretical damping force can be expressed as:…”

Section: The Hbpv Model Of the Yield Stagementioning

confidence: 99%

“…The magnetorheological damper (MRD) is one of the typical representatives of semi-active dampers, and it can control the magnitude of magnetic induction through the external excitation current, thereby adjusting the dynamic shear yield stress of magnetorheological fluid (MRF), so as to achieve the effect of tunable damping characteristics. Due to its fast response speed and low power consumption [3][4][5][6], it has been extensively used in low-frequency vibration fields, such as automotive suspension [7][8][9][10][11], buildings [12][13][14][15][16], bridges [17][18][19][20], prosthetics [21][22][23][24][25], etc.…”

Section: Introductionmentioning

confidence: 99%

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Dual-stage theoretical model of magnetorheological dampers and experimental verification

Lei,

Li,

Zhou

et al. 2024

Smart Mater. Struct.

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The theoretical model for predicting the damping characteristics of magnetorheological dampers (MRDs) not only facilitates the optimization of MRD parameters, but also provides assistance for the theoretical design of MRDs. However, some existing models have limitations in fully characterizing the damping characteristics of MRDs. In this paper, the workingstageof MRDs was categorized into yield and pre-yield stages based on whether the internal magnetorheological fluid (MRF) attains the dynamic shear yield state or not, and the Herschel-Bulkley model with pre-yield viscosity(HBPV) and improved polynomial model (IPOL) were employed to respectively characterize the yield and pre-yield stages of MRDs. Subsequently, the HBPV-IPOL model was proposed to characterize the complete damping characteristics of MRDs in low-frequency vibration conditions, with considering the local loss effect of the fluid in the model. To accurately characterize the magnetic induction intensity in the MRD damping channel, employing the steady-state finite element method (FEM) for magnetic field analysis; on this basis, dividing the damping channel to investigate the variation trends of the magnetic induction intensity in different regions. Simultaneously, the zero-field region hypothesis was proposed to quantitatively consider the influence of minute magnetic induction intensity in the traditional zero-field regions on the damping characteristics of MRDs. Finally, integrating the impact trends of currents in different regions, and employing the HBPV model to determine the impact magnitude of each region within the damping channel on the damping characteristics of the MRD in the yield stage. In the pre-yield stage, Polynomial curves were fitted to experimental damping force-velocity curves, and the obtained polynomials were employed to predict the damping characteristics. Extensive experiments have been conducted on MRD samples to assess the predictive performance of the model on MRD damping characteristics under sinusoidal displacement excitation vibration conditions with different excitation currents, vibration frequencies and vibration amplitudes.

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Section: The Hbpv Model Of the Yield Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dual-stage theoretical model of magnetorheological dampers and experimental verification

Lei,

Li,

Zhou

et al. 2024

Smart Mater. Struct.

View full text Add to dashboard Cite

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“…19 When simulating the performance of a magnetorheological fluid damper under stochastic excitation, the backstepping controller could resolve time delays, noise disturbances, and actuator failures. 20 Scholars designed a delay compensation in model predictive to reduce the current fluctuation. 21 The scholars focused on the design of the MPC control objective function in the buck-boost converter, which can effectively control multiple targets, and the effect is excellent.…”

Section: Introductionmentioning

confidence: 99%

“…A new MPC method can improve the operational capability of the complex multivariate observer operation capability in case of failure 19 . When simulating the performance of a magnetorheological fluid damper under stochastic excitation, the backstepping controller could resolve time delays, noise disturbances, and actuator failures 20 . Scholars designed a delay compensation in model predictive to reduce the current fluctuation 21 .…”

Section: Introductionmentioning

confidence: 99%

Robustness improvement model predictive control strategy based on Luenberger observer for Y‐type modular multilevel converter

Yue

Cheng

2023

Circuit Theory & Apps

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SummaryThe Y‐type modular multilevel converter (Y‐MMC) has high reliability due to its high modularity. It can promise AC/AC conversion for high‐voltage and large‐capacity fractional frequency transmission systems (FFTS). The Y‐MMC under linear control has a poor ability to adapt to changing working conditions, resulting in unstable transmission, slow response, and large harmonics and overshoot. The Robustness Improvement Model Predictive Control strategy (RI‐MPC) for Y‐MMC under nonideal conditions is presented in this paper to enhance robustness under unbalanced conditions and adaptability to load changes. It points out that the conventional MPC has low robustness when Y‐MMC encounters parameters difference due to faulty line, designs an improved outer‐loop PI control to enhance robustness to provide an excellent reference value for the inner‐loop current, combines the Luenberger observer with MPC and limits the parameters with Jury criterion to improve stability, and uses a two‐step prediction to compensate for delay and reduce harmonics. Through software simulation and hardware experiments, it is proved that compared with the traditional PI and MPC method, the RI‐MPC method is more robust, faster, and has application value.

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“…In order to handle the highly nonlinear and hysteretic characteristics of the MRD-AAS system, numerous control methods have been proposed, such as sliding mode control, backstepping control, and fuzzy logic and neural network-based control techniques. The authors of [ 10 ] proposed a compensated backstepping controller combining traditional backstepping techniques with an adaptive radial basis function. In order to estimate unmodeled dynamic characteristics, fuzzy logic systems and neural networks are employed as the approximators for improving tracking control performance [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Adaptive Height Tracking Control of Active Air Suspension System with Magnetorheological Fluid Damper Subject to Uncertain Mass and Input Delay

Zhao,

Xie,

Gong

et al. 2023

Sensors

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In this paper, we present a novel robust adaptive neural network-based control framework to address the ride height tracking control problem of active air suspension systems with magnetorheological fluid damper (MRD-AAS) subject to uncertain mass and time-varying input delay. First, a radial basis function neural network (RBFNN) approximator is designed to compensate for unmodeled dynamics of the MRD. Then, a projector-based estimator is developed to estimate uncertain parameter variation (sprung mass). Additionally, to deal with the effect of input delay, a time-delay compensator is integrated in the adaptive control law to enhance the transient response of MRD-AAS system. By introducing a Lyapunov–Krasovskii (LK) functional, both ride height tracking and estimator errors can robustly converge towards the neighborhood of the desired values, achieving uniform ultimate boundness. Finally, comparative simulation results based on a dynamic co-simulator built in AMESim 2021.2 and Matlab/Simulink 2019(b) are given to illustrate the validity of the proposed control framework, showing its effectiveness to operate ride height regulation with MRD-AAS systems accurately and reliably under random road excitations.

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Design of compensatory backstepping controller for nonlinear magnetorheological dampers

Cited by 10 publications

References 28 publications

Dual-stage theoretical model of magnetorheological dampers and experimental verification

Dual-stage theoretical model of magnetorheological dampers and experimental verification

Robustness improvement model predictive control strategy based on Luenberger observer for Y‐type modular multilevel converter

Neural Network-Based Adaptive Height Tracking Control of Active Air Suspension System with Magnetorheological Fluid Damper Subject to Uncertain Mass and Input Delay

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