Stochastic sliding mode control of active vehicle suspension with mismatched uncertainty and multiplicative perturbations

Moghadam, Alireza Ramezani; Kebriaei, Hamed

doi:10.1002/asjc.2135

Cited by 10 publications

(10 citation statements)

References 34 publications

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“…In [48], Gang proposed using a fully model's SMC algorithm for suspension. Tis algorithm can also be used for half-or quarter-models [49,50]. Te SMC algorithm needs to use the higher-order derivative of the output signals, so the use of a linear model of the actuator is necessary [29].…”

Section: Control Algorithmsmentioning

confidence: 99%

The Dynamic Model and Control Algorithm for the Active Suspension System

Nguyen

2023

Mathematical Problems in Engineering

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Road surface roughness is the leading cause of vehicle oscillation. The suspension system is used to dampen these oscillations. The active suspension system equipped with a hydraulic actuator is more efficient than the passive one. Therefore, it is used to replace the passive suspension system. The article reviews and analyses models and control algorithms for active suspension systems. In this article, the author mentioned three dynamic models commonly used to simulate vehicle oscillations: a quarter-dynamic model, a half-dynamic model, and a fully dynamic model. Each hydraulic actuator can be considered a state variable in the dynamic model. Besides, the control algorithm for the suspension system is significant. Algorithms like PID (proportional–integral–derivative) and LQR (linear quadratic regulator) will fit the linear model. In contrast, the nonlinear model’s algorithms, such as SMC (sliding mode control), fuzzy, and ANN (artificial neural network), will perform better. Overall, vehicle oscillation can be significantly improved once an active suspension system is used. The contents analysed in this article will be the database for selecting control models and algorithms by other researchers in the future.

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Section: Control Algorithmsmentioning

confidence: 99%

The Dynamic Model and Control Algorithm for the Active Suspension System

Nguyen

2023

Mathematical Problems in Engineering

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“…Proof. We choose the Lyapunov function candidate (26) as follows so that the reference state tracking can be illustrated…”

Section: Astringency Of the Smcmentioning

confidence: 99%

“…(ii) Its response speed is fast, and it is robust to external noise interference and parameter perturbation. SMC has been used to handle the model parameter uncertainties and nonlinearities in many complex dynamical systems, for example, [23][24][25][26][27] and the references therein. This is the other one of motivations for our research.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode control for electro‐hydraulic proportional directional valve‐controlled position tracking system based on an extended state observer

Zhuang

Sun

Chen

2020

Asian Journal of Control

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A novel method combining the sliding mode control (SMC) and extended state observer (ESO) is proposed for position control of an electro-hydraulic servo system (EHSS), which is nonlinear in mechanical dynamics. Based on the SMC technique, the corresponding sliding mode controller is designed to guarantee the state variables of the closed-loop system to converge to the reference state by applying an ESO to estimate external disturbances and the internal dynamics. Also, simulation results are presented to illustrate the effectiveness of the control strategy; synchronously, SMC can achieve better control performance than proportional integral derivative (PID) method in comparison with the PID control approach.

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“…The SMC has been successfully applied to a wide variety of complex systems and engineering [2,5]. There are many papers available on the wide utilization of SMC in various industrial applications and practical engineering systems such as robot manipulators and tank level control [22,23]. The SMC has some inherent advantages such as (1) low sensitivity to parameter variations and model uncertainties; (2) external disturbance rejection; and (3) fast dynamic responses with acceptable transient performance [6].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy modeling and control of a class of non‐differentiable multi‐input multi‐output nonlinear systems

Zare

Shasadeghi

Izadian

et al. 2021

Asian Journal of Control

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This paper presents a new approach in modeling and control of multi‐input multi‐output (MIMO) systems that have non‐differentiable operating points. A circle criterion is introduced at the non‐differentiable operating points to divide the entire operating region into two parts. Takagi‐Sugeno fuzzy models are developed in each part, and a switching framework is introduced to model the operating region. Accordingly, a sliding mode controller (SMC) is developed. The proposed modeling and controller are implemented on the benchmark quadruple tank process (QTP). It is demonstrated that the proposed modeling and controller design provides simplicity and universality in a set of nonlinear systems; it is robust with respect to various internal and external disturbances and model uncertainties which allows for accurate regulation and tracking; and it converges in finite time, is capable of controlling nonlinear systems with coupling dynamic, and enables fuzzy modeling of continuous‐time systems without imposing differentiability.

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Stochastic sliding mode control of active vehicle suspension with mismatched uncertainty and multiplicative perturbations

Cited by 10 publications

References 34 publications

The Dynamic Model and Control Algorithm for the Active Suspension System

The Dynamic Model and Control Algorithm for the Active Suspension System

Sliding mode control for electro‐hydraulic proportional directional valve‐controlled position tracking system based on an extended state observer

Fuzzy modeling and control of a class of non‐differentiable multi‐input multi‐output nonlinear systems

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