Multi-Objective Robust Control for Vehicle Active Suspension Systems via Parameterized Controller

Cao, Zhong; Zhao, Wenjing; Hou, Xiaorong; Chen, Zhaohui

doi:10.1109/access.2019.2963359

Cited by 19 publications

(8 citation statements)

References 37 publications

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“…(10) and ( 11) is modeled by the sinusoidal equations given by Eqs. (18) and (19). The performance improvement shown in Figs.…”

Section: Resultsmentioning

confidence: 84%

“…Utkarsh et al [17] proposed a sliding mode control technique based on linear disturbance observer for active suspension systems with nonideal actuators. Cao et al [18] proposed a multiobjective H-infinity parameterized controller using Lyapunov and symbolic computation for vehicle active suspension systems. Na et al [19] developed a novel control technique for active suspension systems with unknown nonlinearities using suspension error for full-car active suspension systems with unknown nonlinearities without function approximation.…”

Section: Introductionmentioning

confidence: 99%

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Pareto optimality based PID controller design for vehicle active suspension system using grasshopper optimization algorithm

Gampa¹,

Mangipudi²,

Jasthi³

et al. 2022

Journal of Electrical Systems and Inf Technol

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In this paper, a Pareto multiobjective and grasshopper optimization algorithm (GOA) based optimum proportional–integral–derivative (P–I–D) controller design is proposed for improving the vehicle active suspension system dynamics under road disturbance conditions. The Pareto objectives considered are minimization of sprung mass suspension deflection, tyre deflection, sprung mass acceleration minimization and eigenvalue-based objective function. State space model for quarter vehicle active suspension system with P–I–D controller is developed for analyzing the stability and dynamic performance of the system. The sinusoidal-based bump road disturbances are used for testing the robustness of the proposed control technique. Simulation results have been presented to show the advantage of the proposed Pareto multiobjective and GOA-based P–I–D controller over the weighted multiobjective and genetic algorithm-based P–I–D controller in terms of stability and dynamics of the active suspension system.

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“…(10) and ( 11) is modeled by the sinusoidal equations given by Eqs. (18) and (19). The performance improvement shown in Figs.…”

Section: Resultsmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Pareto optimality based PID controller design for vehicle active suspension system using grasshopper optimization algorithm

Gampa¹,

Mangipudi²,

Jasthi³

et al. 2022

Journal of Electrical Systems and Inf Technol

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show abstract

“…(ii) Suspension space: In order to prevent structural damage from the collision between the wheel hub and the vehicle body, the suspension movement should be kept to a minimum. This performance indicator can be determined by the ratio of suspension displacement or RSD, and it is expressed as [43] RSD…”

Section: Problem Formulationmentioning

confidence: 99%

Neuroadaptive Natural Logarithm Sliding Mode Control for Nonlinear Active Suspension Systems

Wijaya,

Yakub,

Abdullah

et al. 2024

IEEE Trans. Intell. Veh.

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A neuroadaptive controller based on natural logarithm sliding mode control (lnSMC) is proposed for an active suspension system with unknown nonlinear dynamics and uncertain model parameters. The proposed control scheme ensures that the controlled states are constrained within the desired bound of heave and pitch motions, thereby eliminating the need for trial and error in determining the lnSMC controller parameters. Moreover, the unknown nonlinear system dynamics are approximated by a radial basis function neural network (RBFNN), which updates its weights continuously in real-time. Considering the high degree of parameter uncertainties in suspension systems, an adaptive law based on a gradient algorithm with a projection operator is incorporated to estimate the unknown parameters (e.g., vehicle mass and mass moment of inertia). Simulation studies on a half-car active suspension model are carried out to evaluate the performance and robustness of the proposed controller under various road disturbances, including bumps and random road profiles. For comparative purposes, neuroadaptive controllers based on classical sliding mode and terminal sliding mode are designed as benchmark controllers. The simulation results indicated that the proposed controller achieves a better suspension performance indicators compared to the benchmark controllers.

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“…Therefore, active suspension systems have been admitted its outstanding performance in comfort and vehicle handling to the passengers. In the previous researches, a number of control strategies for AS have been proposed to improve the suspension performance, such as fuzzy neural network control [22][23][24], H ∞ control [25][26][27], model predictive control [28], linear quadratic Gaussian (LQG) control [29], sliding mode control (SMC) [30][31][32], robust control [33,34] etc. Wang et al [28] introduced a robust model predictive controller (RMPC) for a seven DoFs AS system model.…”

Section: Related Workmentioning

confidence: 99%

A reinforcement learning backstepping‐based control design for a full vehicle active Macpherson suspension system

Lin

Nguyen

Yang

et al. 2022

IET Control Theory & Appl

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This paper presents a reinforcement learning backstepping-based control scheme for the design of a full vehicle active suspension system such that both the superior ride comfort and stabilization are achieved. It is well known that the traditional backstepping control scheme is suitable to solve higher order nonlinear system based on the strict-feedback form; however, the disadvantage of the procedure requires computing the derivatives of virtual control signals, which is a very complex task. Therefore, a reinforcement learning scheme using the deep deterministic policy gradient (DDPG) control strategy is developed to replace the process of finding virtual control force in backstepping method. It not only avoids the complexity of analytically calculating the derivatives of the virtual control signals, but also retains the systems robustness when there exist random disturbances from road irregularities. To verify the performance of the proposed active suspension control scheme, the random road unevenness profiles according to ISO 8608 is considered as vertical and lateral disturbance input excitation of a full-vehicle suspension system. Compared with conventional passive suspension system and backstepping control scheme, the proposed approach can be demonstrated to not only effectively improve ride comfort under random road excitation, but also improve the transient response and robustness.

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Multi-Objective Robust Control for Vehicle Active Suspension Systems via Parameterized Controller

Cited by 19 publications

References 37 publications

Pareto optimality based PID controller design for vehicle active suspension system using grasshopper optimization algorithm

Pareto optimality based PID controller design for vehicle active suspension system using grasshopper optimization algorithm

Neuroadaptive Natural Logarithm Sliding Mode Control for Nonlinear Active Suspension Systems

A reinforcement learning backstepping‐based control design for a full vehicle active Macpherson suspension system

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