Road-adaptive algorithm design of half-car active suspension system

Huang, Chia-Yu; Lin, Jung-Shan; Chen, Chung‐Cheng

doi:10.1016/j.eswa.2009.11.089

Cited by 44 publications

(20 citation statements)

References 15 publications

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“…The physical values of vehicle suspension parameters are presented in Table 1. These values are related to a typical light commercial car and have been selected near the values of different half car models reported in prevalent papers [7,9,14].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Kim and Ro [7] compared the responses of linear and non-linear suspension models for different road inputs and concluded that the non-linear characteristics of suspension elements should be considered in developing a more accurate system model. There are many studies that have used a half-car model but with linear elements [8][9][10][11]. Generally, a nonlinear half-car model considering pitch and bounce motions needs to be developed for the design of active suspension systems.…”

Section: Introductionmentioning

confidence: 99%

“…Commonly used control approaches of suspension systems include sliding mode control [7,[12][13][14], non-linear adaptive control [9,15], fuzzy logic control [8,15,16,17], back-stepping control [18,19], multi-objective robust control [20,21], H ∞ control [4,22,23], LQ-based control [24], LQG control approach [25,26] and feedback linearization control [27]. Each of these control methods considers some aspect of active suspension problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Non-Linear Predictive Control of Multi-Input Multi-Output Vehicle Suspension System

Malekshahi

Mirzaei

Aghasizade

2015

Journal of Low Frequency Noise, Vibration and Active Control

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A new predictive control approach is employed to control a non-linear 4 degrees of freedom vehicle suspension model including pitch and bounce motions. The front and rear suspension actuator forces are considered as control inputs. These are determined by minimizing a performance index defined as a weighted combination of conflicting objectives, namely ride comfort and road holding. In this way, the suspension deflection and the control forces should be restricted to admissible practical ranges. In this paper, an optimization process is used to develop a multi-input multi-output (MIMO) non-linear control law in a closed-form which is mathematically tractable and suitable for implementation. To investigate special features of the derived control law, the closed-loop system dynamics are first evaluated analytically and then the corresponding simulation results are presented. The results show that a compromise between mentioned objectives and control energy, even in the presence of uncertainties, can be easily made by regulating the control weighting factors.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Non-Linear Predictive Control of Multi-Input Multi-Output Vehicle Suspension System

Malekshahi

Mirzaei

Aghasizade

2015

Journal of Low Frequency Noise, Vibration and Active Control

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“…A review of computational intelligence involved approaches such as Neural Network (NN), Fuzzy Logic (FL), Genetic Algorithm (GA) and their combination for active systems have been detailed in [9]. A valuable roadadaptive nonlinear control [4] which continuously monitors suspension travel and adjusts online, the shape of the filter nonlinearity in response to different road profiles has been described and compared with back stepping control and optimal control for active suspension HC model. In [1], the control of quarter car and HC model with optimum damping coefficients with fixed other system parameters has been explained by state space approach of the system.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of travelling comfort using PID based adaptive neuro fuzzy inference system for half car model

Kalaivani

Lakshmi²,

Sneha

2013

IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013)

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In this paper, pioneering Adaptive Neuro Fuzzy Inference System (ANFIS) which is trained with well known traditional Proportional, Integral and Derivative (PID) controller data for Half Car (HC) model is proposed to improve the travelling comfort. The travelling performance is generally assessed at the design stage in automobile industries by simulating the vehicle response to various road excitations. In this work, the disturbance from the road is assumed to be a jerk which represents a sudden shock. Initially a PID controller is designed and tuned to give better response. Secondly the PID tuning parameters are optimized using popular stochastic global optimization technique -Genetic Algorithm (GA) by considering the body acceleration as the Performance Index (PI). At last, an ANFIS which inherits the features of Neural Network (NN) and Fuzzy Logic (FL) is used for control purpose. The modelling of system and simulation with and without controllers are carried out in MATLAB/ Simulink environment. A comparison is made among the responses obtained and it shows that, the system with PID based ANFIS gives significant reduction of the Sprung mass Displacement (SD) and thus improves the travelling comfort.

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“…Application of Newton's second law of motion to the nonlinear half-car AVSS gives the governing equations of motion in state-space form as (Szaszi et al, 2002;Huang et al, 2010) …”

Section: Physical and Mathematical Modellingmentioning

confidence: 99%

Proportional-integral-derivative control of nonlinear half-car electro-hydraulic suspension systems

Ekoru

Pedro

2013

J. Zhejiang Univ. Sci. A

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This paper presents the development of a proportional-integral-derivative (PID)-based control method for application to active vehicle suspension systems (AVSS). This method uses an inner PID hydraulic actuator force control loop, in combination with an outer PID suspension travel control loop, to control a nonlinear half-car AVSS. Robustness to model uncertainty in the form of variation in suspension damping is tested, comparing performance of the AVSS with a passive vehicle suspension system (PVSS), with similar model parameters. Spectral analysis of suspension system model output data, obtained by performing a road input disturbance frequency sweep, provides frequency response plots for both nonlinear vehicle suspension systems and time domain vehicle responses to a sinusoidal road input disturbance on a smooth road. The results show the greater robustness of the AVSS over the PVSS to parametric uncertainty in the frequency and time domains.

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Road-adaptive algorithm design of half-car active suspension system

Cited by 44 publications

References 15 publications

Non-Linear Predictive Control of Multi-Input Multi-Output Vehicle Suspension System

Non-Linear Predictive Control of Multi-Input Multi-Output Vehicle Suspension System

Enhancement of travelling comfort using PID based adaptive neuro fuzzy inference system for half car model

Proportional-integral-derivative control of nonlinear half-car electro-hydraulic suspension systems

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