Self-tuned robust fractional order fuzzy PD controller for uncertain and nonlinear active suspension system

Kumar, Vineet; Rana, K.P.S.; Kumar, Jitendra; Mishra, Puneet

doi:10.1007/s00521-016-2774-x

Cited by 49 publications

(19 citation statements)

References 34 publications

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“…The derivations of mathematical equation of nonlinear active suspension system of quarter vehicle system are given through Eqs. (1) and (2).…”

Section: Mathematical Models 21 Mathematical Modelling Of Nonlinear mentioning

confidence: 99%

Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear Quarter Car Active Suspension System

2020

JIEA

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Recently, active suspension system will become important to the vehicle industries because of its advantages in improving road managing and ride comfort. This paper offers the development of mathematical modelling and design of a neural network control approach. The paper will begin with a mathematical model designing primarily based at the parameters of the active suspension system. A nonlinear three by four-way valve-piston hydraulic actuator became advanced which will make the suspension system under the active condition. Then, the model can be analyzed thru MATLAB/Simulink software program. Finally, the NARMA-L2, model reference and predictive controllers are designed for the active suspension system. The results are acquired after designing the simulation of the quarter-car nonlinear active suspension system. From the simulation end result using MATLAB/Simulink, the response of the system might be as compared between the nonlinear active suspension system with NARMA-L2, model reference and predictive controllers. Besides that, the evaluation has been made between the proposed controllers thru the characteristics of the manage objectives suspension deflection, body acceleration and body travel of the active suspension system.. As a conclusion, designing a nonlinear active suspension system with a nonlinear hydraulic actuator for quarter car model has improved the car performance by using a NARMA-L2 controller. The improvements in performance will improve road handling and ride comfort performance of the active suspension system.

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“…The derivations of mathematical equation of nonlinear active suspension system of quarter vehicle system are given through Eqs. (1) and (2).…”

Section: Mathematical Models 21 Mathematical Modelling Of Nonlinear mentioning

confidence: 99%

Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear Quarter Car Active Suspension System

2020

JIEA

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“…As the passive suspension has fixed characteristics and is subject to disability to add energy, it does not involve a feedback control mechanism. 6,7 Previous studies 8,9 proved that the passive suspension systems are able to improve the ride quality but any responses of road and load disturbances cannot be fully controlled independently by any passive suspension system. Meanwhile, a semi-active system has a spring and controllable damper where the element of spring is used to store energy whereas the controllable damper is used to dissipate energy.…”

Section: Introductionmentioning

confidence: 99%

Control analysis of vehicle ride comfort through integrated control devices on the quarter and half car active suspension systems

Munawwarah

Yakub

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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This study proposed an integrated chassis control of the quarter car and half car active suspension systems to enhance vehicle ride comfort and road handling performance. The integrated controls of PID-LQR and Fuzzy-PID were designed to maximize driving comfort by keeping the vehicle wheels in contact with the road surface. The PID control which is commonly used in car manufacturing was set as a controller benchmark. Multiple road conditions such as speed bumps of different heights, 0.1 m < h < 0.3 m and widths, 0.3 m < d < 0.5 m were used in the system for analysis. In comparison with PID-LQR, Fuzzy-PID showed the lowest peak amplitude and reached the stability state in a minimum time against the controller benchmark. This indicates that vehicle ride comfort and road handling performance can be optimized using the proposed Fuzzy-PID control.

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“…Khodadadi and Ghadiri [15] used the Proportional Integral Derivative (PID), fuzzy logic and controllers to control the car suspension system based on the half car model and developed a self-tuning PID controller based on the fuzzy logic to improve the performance of the system. Kumar et al [16] proposed a self-tuned robust fractional order fuzzy proportional derivative (FO-FPD) controller for a nonlinear active suspension system of a quarter car. Liang et al [17] developed a technique of optimal vibration control and simulation for vehicle active suspension systems and established a mechanical model and nonlinear dynamic system for a class of tracked vehicle half-car suspensions vibration control considered the nonlinear damping of springs.…”

Section: Introductionmentioning

confidence: 99%

Collaborative model analysis on ride comfort and handling stability

Liu¹,

Cui²

2019

J. vibroeng.

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For considering the connection and mutual influences between ride comfort and handing stability of a vehicle, a collaborative study on the two performances is carried out in the paper. Firstly based on the UniTire model and combined with filtered white noise models for front and real road excitations, 4-DOF plane model for ride comfort and 2-DOF plane model for handling stability, a collaborative model for ride comfort and handing stability is built by adopting state equations, and a collaborative simulation algorithm for them is also proposed. Then, a collaborative simulation on the ride comfort and handing stability of a vehicle under common road grade and speed conditions is conducted. The results show that the ride comfort and handling stability of a vehicle can be simulated simultaneously by using the collaborative model. The handling stability parameters simulated with the linear UniTire model are larger than those simulated with the nonlinear UniTire model, indicating that it is obviously conservative to study vehicle handling stability with the linear UniTire model.

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Self-tuned robust fractional order fuzzy PD controller for uncertain and nonlinear active suspension system

Cited by 49 publications

References 34 publications

Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear Quarter Car Active Suspension System

Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear Quarter Car Active Suspension System

Control analysis of vehicle ride comfort through integrated control devices on the quarter and half car active suspension systems

Collaborative model analysis on ride comfort and handling stability

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