Review on Modeling and Controller Design of Hydraulic Actuator Systems

Salleh, Shaharuddin; Rahmat, M. F.; Othman, Siti Marhainis; Danapalasingam, Kumeresan A.

doi:10.21307/ijssis-2017-762

Cited by 13 publications

(9 citation statements)

References 57 publications

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“…The suspension system serves as a critical component to the vehicle system, as it ensures the steering stability and sound ride quality of the vehicle. Several means of control strategies have been developed to realize desirable suspension performance [1], [2], of which actuators (e.g., hydraulic actuators [3], magnetorheological dampers [4]) are utilized to adjust tunable system parameters according to different driving The associate editor coordinating the review of this manuscript and approving it for publication was Ah Hwee Tan . conditions.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Estimation of the Unknown Road Profile and State Variables for the Vehicle Suspension System

Kim

Lee

et al. 2021

IEEE Access

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The vehicle suspension control unit serves as a critical component to the vehicle system, as it ensures the steering stability and sound ride quality of the vehicle. To effectively realize control strategies, it is essential to foreknowledge the road profile and the suspension system's internal state variables. While the mentioned variables are not practically measurable using commercial sensors, it is necessary to estimate the desired variables by utilizing observer systems. Conventional means have mainly employed modelbased approaches, in which model uncertainties and high computational cost pose limitations for practical implementation. Herein, we propose a data-driven deep learning method as an alternative because no explicit physical modeling is required, and evaluation is computationally cheap. We first propose a novel encoderdecoder structured recurrent neural network model with a two-phase attention mechanism to estimate the unknown road profile and four state variables of the vehicle suspension system. Based on a simulated data set, we assess the proposed model's qualitative and quantitative results and demonstrate that our model can achieve highly accurate estimation results with fast computation time. Besides, we validate our black-box model's reliability by comparing its interpretation with the suspension system's actual physical characteristics. Furthermore, we compare the proposed model with existing baseline methods, and the results show that our proposed deep learning model significantly outperforms the baseline. Lastly, we experiment with our network's autoregressive capability and demonstrate the feasibility of estimating a sequence of future values, which has not been presented in previous works.INDEX TERMS Deep learning, long short term memory (LSTM), observer systems, recurrent neural networks (RNNs), vehicle suspension control.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Estimation of the Unknown Road Profile and State Variables for the Vehicle Suspension System

Kim

Lee

et al. 2021

IEEE Access

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“…By solving a non-linear transcendental equation with a modulation index both of angles θ 1 and θ 2, can be selected. a modulation index can be obtained be using formula (5).…”

Section: Furrier Series Equationmentioning

confidence: 99%

“…Therefore, increase number of switching PWM leads to increase output voltage level and then leads to high cost for circuit [4]. However, the multilevel inverter has limitation harmonic order in 7-level 3th, 5th by using optimization technique are PSO, NR and GA algorithms are present in [5]. In nine level multilevel inverter Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752 …”

Section: Introductionmentioning

confidence: 99%

Enhance the accuracy of control algorithm for multilevel inverter based on artificial neural network

Rasheed

Alakkad

Omar

et al. 2020

IJEECS

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In converters or multilevel inverters it is very important to ensure that the output of the multilevel inverters waveforms in term of the voltage or current of the waveforms is smooth and without distortion. The artificial neural network (ANN) technique to obtaining proper switching angles sequences for a uniform step asymmetrical modified multilevel inverter by eliminating specified higher-order harmonics while maintaining the required fundamental voltage and current waveform. However, through this paper a modified CHB-MLI are proposed using artificial intelligence optimization technique based on modulation Selective Harmonic Elimination (SHE-PWM). A most powerful modulation technique that used to minimize a harmonic contants during the outout waveform of multilevel inverter is a SHE-PWM method. The proposed a five-level Modified Cascaded H-Bridge Multilevel Inverter (M-CHBMI) with ANN controller to improve the output voltage and current performance and achieve a lower Total Harmonic Distortion (THD). The main aims of this paper cover design, modeling, prediction for real-time generation of optimal switching angles in a single-phase topology of modified five level CHB-MLI. due to the heavy cost of computation to solving transcendental nonlinear equations with specified number, a real-time application of Selective Harmonic Elimination-Pulse Width Modulation (SHE-PWM) technique is limited. SHE equations known as a transcendental nonlinear equation that contain trigonometric functions. The prototype of a 5-level inverter in Digital Signal Processing (DSP) TMS320F2812 reveals that the proposed method is highly efficient for harmonic reduction in modified multilevel inverter.

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“…Although the EHSS has several benefits in engineering applications, the EHSS is known as a highly nonlinear dynamic system and largely modeled uncertainties of parametric uncertainties and uncertain nonlinearities (Sallah et al, 2015). In Alleyne and Liu (2000), a Lyapunov-based control was developed to compensate friction for an EHSS.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network and nonlinear electrohydraulic active suspension control system

Aela

Kenné

Mintsa

2020

Journal of Vibration and Control

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In this article, an adaptive neural network control system is proposed for a quarter car electrohydraulic active suspension system coping with dynamic nonlinearities and uncertainties. The proposed control system is primarily designed to stabilize a sprung mass position of the quarter car electrohydraulic active suspension. Linear controllers such as the proportional–integral–differential controller have limited control performances. The limited control performances are caused by dynamic phenomena such as nonlinearity, parametric uncertainties, and stiff external disturbances. To overcome these dynamic phenomena, we propose a combined adaptive radial basis function neural network with a backstepping control system for a quarter car active suspension system. This setup can handle the unmatched model uncertainty of the system, while the adaptive neural network can take care of its unknown smoothing functions. In general, radial basis function neural network can represent a complicated function, and therefore, semi-strict-feedback dynamic systems are considered to simplify the adaptive neural network control design. Simulation results are indicated to illustrate adaptive neural network control effectiveness.

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Review on Modeling and Controller Design of Hydraulic Actuator Systems

Cited by 13 publications

References 57 publications

Deep Learning-Based Estimation of the Unknown Road Profile and State Variables for the Vehicle Suspension System

Deep Learning-Based Estimation of the Unknown Road Profile and State Variables for the Vehicle Suspension System

Enhance the accuracy of control algorithm for multilevel inverter based on artificial neural network

Adaptive neural network and nonlinear electrohydraulic active suspension control system

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