Robustness Analysis of the Model Reference Control for Active Suspension System

Kaldas, Mina M.S.; Soliman, Ahmed; Abdallah, Sayed A.; Amien, Fomel F.

doi:10.4271/10-04-02-0012

Cited by 8 publications

(2 citation statements)

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“…Although the above control methods achieved a good control effect for the working conditions of a single constant, they did not consider the complex and variable conditions of the control problem. MM Kaldas et al [ 12 ] used optimization algorithms based on the gradient algorithm to evaluate the performance of a controller in different driving conditions and a model of an active suspension controller for analytical research; their simulation studies show that the controller has an effect on the vehicle’s comfort and grip in different working conditions. Guo et al [ 13 ] proposed an active suspension control strategy based on an inertial measurement unit as a way to compensate the body attitude and improve the ride comfort and operational stability of a vehicle on gravel and sloped roads.…”

Section: Introductionmentioning

confidence: 99%

Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control

Hu,

Liu,

Wang

et al. 2024

Sensors

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In this study, an electric oil and gas actuator based on fractional-order PID position feedback control is proposed, through which the damping coefficient of the suspension system is adjusted to realize the active control of the suspension. An FOPID algorithm is used to control the motor’s rotational angle to realize the damping adjustment of the suspension system. In this process, the road roughness is collected by the sensors as the criterion of damping adjustment, and the particle swarm algorithm is utilized to find the optimal objective function under different road surface slopes, to obtain the optimal cornering value. According to the mathematical and physical model of the suspension system, the simulation model and the corresponding test platform of this type of suspension system are built. The simulation and experimental results show that the simulation results of the fractional-order nonlinear suspension model are closer to the actual experimental values than those of the traditional linear suspension model, and the accuracy of each performance index is improved by more than 18.5%. The designed active suspension system optimizes the body acceleration, suspension dynamic deflection, and tire dynamic load to 89.8%, 56.7%, and 73.4% of the passive suspension, respectively. It is worth noting that, compared to traditional PID control circuits, the FOPID control circuit designed for motors has an improved control performance. This study provides an effective theoretical and empirical basis for the control and optimization of fractional-order nonlinear suspension systems.

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Section: Introductionmentioning

confidence: 99%

Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control

Hu,

Liu,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Shirahatt et al developed a mathematical FCM with 8 DOF with the addition of driver seat vertical motion and investigated comfort parameters such as maximum vertical acceleration of the passenger seat and tire displacement considering ISO2631 standard and optimized suspension system parameters using genetic algorithm [9]. Kaldas et al developed a 9 DOF HCM with the addition of driver seat and engine vertical motion to the classical 7 DOF FCM for a passive suspension system vehicle and 13 DOF HCM with the addition of the 4 actuator positions for an active suspension vehicle [10]. They employed a Model Reference Controller (MRC) that contains 8 Proportional Integral Derivative (PID) controllers for both body and wheel control.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Suspension System Parameters for a SUV

Ötkür¹,

Abdullah²,

Alshammari³

et al. 2022

Proceedings of the 8th World Congress on Mechanical, Chemical, and Material Engineering

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Ride comfort has been an important development parameter for transport vehicles starting from early horse carriages with simple leaf spring suspension systems, up to modern vehicles with the state-of-the-art suspension systems. A vehicle without a suspension system will transfer all the disturbances caused by bumps or holes on the road resulting to high acceleration and jerk values at the passenger compartment. Suspension system acts as the cushion of the vehicle when it undergoes road irregularities improving passenger comfort. Softer suspension systems provide better ride comfort via reducing the magnitude of the chassis oscillations however have negative effect on vehicle dynamics considering the fact that they result with loss of traction due to excessive roll motion of the vehicle causing weight transfer from the inner wheels to the outer wheels during cornering manoeuvres. Hence, optimization of suspension system parameters is essential considering both vehicle comfort and dynamics. Similar to all mechanical components, optimization using real hardware is considerably expensive and time consuming. Therefore, model-based optimization is essential to obtain the best performance parameters considering objectives as follows: minimize acceleration magnitude and pitch angle. Within this study a Half Car Model (HCM) for vehicle suspension system is developed in MATLAB / Simulink software and parameters used in the model are tuned for a Sport Utility Vehicle (SUV) using measurements captured via MATLAB Mobile software employed in a mobile phone. A full factorial Design of Experiment (DOE) is developed spanning ± %20 of original values. A regression model is built in Minitab software and it has been showed that optimized parameters result with %3.4 and 9.4% reduction in pitch angle and maximum acceleration values respectively.

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Modeling and state-preview height control of electronically controlled air suspension system based on experiment

Long

Tan

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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In the control of electronically controlled air suspension system (ECAS), the precision and stability of height control are important targets to measure the efficiency. In the control process of an ECAS system, the phenomenon of “overinflation,”“overdeflation,” and frequent inflating and deflating is commonly existed, which is rooted in the hysteresis characteristic of the system. Based on the bench test, the system identification and quantitative performance analysis of solenoid valves and air springs were carried out, and a refined ECAS system model was built. A PID control strategy is optimized and a state-preview height control strategy is established. AMESim and Simulink are used to build the vehicle and controller models. The accuracy of the model is verified by the comparison between the co-simulation and the field test, and the conclusion is drawn that the state preview height control strategy can improve the smoothness of the height control.

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Robustness Analysis of the Model Reference Control for Active Suspension System

Cited by 8 publications

References 0 publications

Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control

Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control

Optimization of Suspension System Parameters for a SUV

Modeling and state-preview height control of electronically controlled air suspension system based on experiment

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