Hybrid Sliding Mode Control of Full-Car Semi-Active Suspension Systems

Aljarbouh, Ayman; Fayaz, Muhammad; Qureshi, Muhammad Shuaib; Boujoudar, Younes

doi:10.3390/sym13122442

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…To investigate the ride comfort of road vehicles, various dynamic models have been generated and used. In particular, to study the ride comfort and handling characteristics of a passenger car, a 7 degrees of freedom (DOF) 'full-car' model was proposed [4,25]. In this model, the bounce, roll, and pitch of the vehicle body, as well as the bounce of the four wheels are considered.…”

Section: Vehicle Modelmentioning

confidence: 99%

“…In contrast, active vehicle suspensions (AVSs) adjust these properties to external disturbances in a broadband frequency range for improving ride comfort and handling performance [2]. Semi-active suspensions provide a compromised solution between passive and active suspensions in terms of performance and cost (i.e., energy consumption) [3,4].…”

Section: Introductionmentioning

confidence: 99%

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A Design Synthesis Method for Robust Controllers of Active Vehicle Suspensions

Zhu

2022

Designs

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This paper presents a design synthesis method for robust controllers of active vehicle suspensions (AVSs). Various control techniques have been applied to the design of AVSs for enhancing ride comfort and handling performance of ground vehicles. However, most of these model-based controller designs show poor robustness when the vehicle models are not accurate and operating conditions vary. To address the poor robustness problem of AVSs, a new controller is designed using the H∞ loop-shaping control technique. The controller targets robustness issues on vehicle models with parametric uncertainties and unmodelled dynamics. To facilitate the robust controller design, a design synthesis method is proposed: the H∞ loop-shaping controller design is formulated as a multi-objective optimization problem, the weighting functions’ parameters of the controller are treated as design variables, the expensive computing loads are handled by a parallel computing technique, and the solution of the optimization problem is the desired robust AVS controller. Simulation results demonstrate the benefits of the proposed AVS design.

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Section: Vehicle Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Design Synthesis Method for Robust Controllers of Active Vehicle Suspensions

Zhu

2022

Designs

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“…Sliding mode control achieves precise control of a system by introducing a sliding mode surface. The sliding mode surface usually consists of the system state variables and certain related parameters with good response characteristics and stability [21]. Optimal control refers to finding an optimal control scheme from a class of permissible control schemes, so that the motion of the system is transferred from an initial state to a specified target state, while the value of its performance index is optimal.…”

Section: Introductionmentioning

confidence: 99%

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

Yin,

Wang,

et al. 2024

WEVJ

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The suspension system is a crucial part of an electric vehicle, which directly affects its handling performance, driving comfort, and driving safety. The dynamics of the 8-DoF full-vehicle suspension with seat active control are established based on rigid-body dynamics, and the time-domain stochastic excitation model of four tires is constructed by the filtered white noise method. The suspension dynamics model and road surface model are constructed on the Matlab/Simulink simulation software platform, and the simulation study of the dynamic characteristics of active suspension based on the fractional-order PIλDµ control strategy is carried out. The three performance indicators of acceleration, suspension dynamic deflection, and tire dynamic displacement are selected to construct the fitness function of the genetic algorithm, and the structural parameters of the fractional-order PIlDm controller are optimized using the genetic algorithm. The control effect of the optimized fractional-order PIlDm controller based on the genetic algorithm is analyzed by comparing the integer-order PID control suspension and passive suspension. The simulation results show that for optimized fractional-order PID control suspension, compared with passive suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions reaches over 25%, the average optimization of suspension dynamic deflection exceeds 30%, and the average optimization of tire dynamic displacement is 5%. However, compared to the integer-order PID control suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions decreased by 5%, the average optimization of suspension dynamic deflection increased by 3%, and the average optimization of tire dynamic displacement increased by 2%.

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“…The full-car model, which exhibits seven degrees of freedom, allows one to map all major phenomena of vehicle vibration, including the heave, pitch, and roll motion of the vehicle body, as well as the vertical motion of each wheel. Such a model is widely used in the literature, e.g., for the validation of hybrid sliding mode control dedicated to the semi-active suspension system [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Control-Related Signal Path for Semi-Active Vehicle Suspension with Magnetorheological Dampers

Krauze

2023

Sensors

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This paper presents a method for the identification of control-related signal paths dedicated to a semi-active suspension with MR (magnetorheological) dampers, which are installed in place of standard shock absorbers. The main challenge comes from the fact that the semi-active suspension needs to be simultaneously subjected to road-induced excitation and electric currents supplied to the suspension MR dampers, while a response signal needs to be decomposed into road-related and control-related components. During experiments, the front wheels of an all-terrain vehicle were subjected to sinusoidal vibration excitation at a frequency equal to 12 Hz using a dedicated diagnostic station and specialised mechanical exciters. The harmonic type of road-related excitation allowed for its straightforward filtering from identification signals. Additionally, front suspension MR dampers were controlled using a wideband random signal with a 25 Hz bandwidth, different realisations, and several configurations, which differed in the average values and deviations of control currents. The simultaneous control of the right and left suspension MR dampers made it necessary to decompose the vehicle vibration response, i.e., the front vehicle body acceleration signal, into components related to the forces generated by different MR dampers. Measurement signals used for identification were taken from numerous sensors available in the vehicle, e.g., accelerometers, suspension force and deflection sensors, and sensors of electric currents, which control the instantaneous damping parameters of MR dampers. The final identification was carried out for control-related models evaluated in the frequency domain and revealed several resonances of the vehicle response and their dependence on the configurations of control currents. In addition, the parameters of the vehicle model with MR dampers and the diagnostic station were estimated based on the identification results. The analysis of the simulation results of the implemented vehicle model carried out in the frequency domain showed the influence of the vehicle load on the absolute values and phase shifts of control-related signal paths. The potential future application of the identified models lies in the synthesis and implementation of adaptive suspension control algorithms such as FxLMS (filtered-x least mean square). Adaptive vehicle suspensions are especially preferred for their ability to quickly adapt to varying road conditions and vehicle parameters.

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Hybrid Sliding Mode Control of Full-Car Semi-Active Suspension Systems

Cited by 15 publications

References 30 publications

A Design Synthesis Method for Robust Controllers of Active Vehicle Suspensions

A Design Synthesis Method for Robust Controllers of Active Vehicle Suspensions

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

Identification of Control-Related Signal Path for Semi-Active Vehicle Suspension with Magnetorheological Dampers

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