Adaptive neural network and nonlinear electrohydraulic active suspension control system

Aela, Amhmed Mohamed Al; Kenné, Jean‐Pierre; Mintsa, Honorine Angue

doi:10.1177/1077546320975979

Cited by 37 publications

(16 citation statements)

References 33 publications

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“…Its efficiency is also quite high [30][31][32]. Furthermore, ANN-based algorithms improve control process stability in a variety of situations [33,34].…”

Section: Introductionmentioning

confidence: 99%

Advance the Efficiency of an Active Suspension System by the Sliding Mode Control Algorithm With Five State Variables

Nguyen

2021

IEEE Access

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Roughness on the road is the main cause of the oscillation when the vehicle travels. The suspension system plays a role in maintaining the stability of the vehicle in fluctuating states. To improve comfort and smoothness, the active suspension system is used to replace the conventional passive suspension system. The performance of the active suspension system depends on the pre-designed controller. Linear control algorithms cannot guarantee oscillation requirements in all cases. Therefore, the use of a nonlinear control method is necessary. In this paper, the SMC control algorithm is proposed to control the operation of the active suspension system. To optimize this algorithm, five state variables were considered. Besides, the output signal of the model has been taken into account by the fifth derivative, and the error signal is also considered the fourth derivative. This is a completely novel and unique method. The simulation is done in the Matlab-Simulink environment. According to this result, the displacement and acceleration of the sprung mass was significantly reduced when the vehicle used the active suspension system controlled by the SMC algorithm. These values only reach 14.4% and 14.1% compared to the case of the vehicle using the mechanical suspension system. The effect of the SMC algorithm is extremely positive. This will be the basis for developing more complex control algorithms in the future.

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“…Its efficiency is also quite high [30][31][32]. Furthermore, ANN-based algorithms improve control process stability in a variety of situations [33,34].…”

Section: Introductionmentioning

confidence: 99%

Advance the Efficiency of an Active Suspension System by the Sliding Mode Control Algorithm With Five State Variables

Nguyen

2021

IEEE Access

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“…According to Mrazgua, the output signal of the controller is determined based on the fuzzy rule, which has been established previously (Mrazgua et al, 2021). Besides, the neural control algorithms also have positive effects on the active suspension system (Liu et al, 2019;Aela et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Novel Sliding Mode Control Algorithm for an Active Suspension System Considering with the Hydraulic Actuator

Nguyen

Dang

2022

Lat. Am. j. solids struct.

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The suspension system has the role of regulating and extinguishing oscillations in the vehicle. To improve stability and comfort, the active suspension system is proposed to replace the passive suspension system. There are many algorithms used for active suspension system control, such as PID, LQR, Fuzzy, etc. Among them, the nonlinear control method which uses the SMC algorithm gives a stable performance. This research proposes the use of the SMC algorithm to control the operation of the active suspension system equipped with a quarter dynamics model. The process of linearization of the hydraulic actuator is presented in the paper. As a result of the simulation, the values of displacement and acceleration of the sprung mass were significantly reduced when the vehicle used the active suspension system controlled by the SMC algorithm. The SMC controller established in this paper provides stability in many situations. Therefore, the vehicle's smoothness and comfort have been significantly improved. In the future, intelligent algorithms can be combined with SMC algorithms to improve the efficiency of the controller.

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“…SMC with a disturbance observer is proposed by Qin et al 9 for suppressing the vibrations in an ASS. A radial basis function neural network (NN) control 10 and a robust adaptive control method using backstepping and NN identification 11 are proposed for the control of a quarter car ASS. Hon et al 12 proposed a control method based on H ∞ and verified the scheme under various traffic conditions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive sliding mode-based active disturbance rejection control for vehicle suspension control

Suhail

Bazaz

Hussain

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents an adaptive sliding mode-based active disturbance rejection control strategy for the quarter car active suspension control. The suspension system is problematic for its nonlinearities from the dampers, springs, and irregular excitation from the road surface. This study considers this complex behavior and nonlinear characteristics of the suspension system and uses an adaptive sliding mode control law and an extended state observer to estimate and eliminate them from the controlled system. The central concept is to combine the advantages of adaptive sliding mode control to accurately track the reference trajectory with the ability of active disturbance rejection control to reject the parameter uncertainties and external disturbances. The proposed control scheme is verified in simulation studies with parameter uncertainties, nonlinearities, and external disturbances. The simulation results show that the proposed control scheme can significantly improve vehicles’ ride comfort and road-handling ability, owing to the extended state observer’s estimation capability. A comparison of the proposed strategy is also presented with a linear active disturbance rejection control and a conventional proportional–integral–derivative controller. Simulation results show that adaptive sliding mode-based active disturbance rejection improves the robustness against modeling errors, nonlinearities, and disturbances.

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Adaptive neural network and nonlinear electrohydraulic active suspension control system

Cited by 37 publications

References 33 publications

Advance the Efficiency of an Active Suspension System by the Sliding Mode Control Algorithm With Five State Variables

Advance the Efficiency of an Active Suspension System by the Sliding Mode Control Algorithm With Five State Variables

A Novel Sliding Mode Control Algorithm for an Active Suspension System Considering with the Hydraulic Actuator

Adaptive sliding mode-based active disturbance rejection control for vehicle suspension control

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