Disturbance Observer Based Optimal Controller Design for Active Suspension Systems

Sever, Mert; Yazıcı, Hakan

doi:10.1016/j.ifacol.2016.07.505

Cited by 13 publications

(11 citation statements)

References 15 publications

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“…Most of the passive seat suspension research focuses on the optimization of damping coefficients and spring stiffness without external inputs [8]. Semi-active seat suspensions are one kind of controllable suspension systems, which are able to adjust coefficients of dampers according to road surface conditions and vehicle body posture perceived by sensors, in order to improve smooth and stable driving performance of vehicles [9].…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Sliding Mode PI Control for Active Vehicle Seat Suspension Systems

Ning

2019

2019 Chinese Control and Decision Conference (CCDC)

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In this paper, a robust adaptive sliding mode proportional integral control (RASMPIC) method is proposed for an active vehicle seat suspension system, where the driver's mass is supposed as an unknown parameter with boundaries. A dynamic active seat suspension system is established at first. Then a sliding mode controller (SMC) is designed to achieve the required ride comfort performance based on the driver's mass estimated by utilizing an adaptive law where a projecting adaptive algorithm is used to prevent the estimated parameters from surpassing their boundaries to enhance the robustness of the seat suspension system. Also, a proportional and integral (PI) control for driver's acceleration is added into the controller as RASMPIC for the stabilization of the proposed suspension system. In simulations, sinusoidal vibrations are used to test the controllers and the results show the RASMPIC has a better control performance to reduce driver's acceleration and improve the driving comfort than SMC and RASMC.Abstract: In this paper, a robust adaptive sliding mode proportional integral control (RASMPIC) method is proposed for an active vehicle seat suspension system, where the driver's mass is supposed as an unknown parameter with boundaries. A dynamic active seat suspension system is established at first. Then a sliding mode controller (SMC) is designed to achieve the required ride comfort performance based on the driver's mass estimated by utilizing an adaptive law where a projecting adaptive algorithm is used to prevent the estimated parameters from surpassing their boundaries to enhance the robustness of the seat suspension system. Also, a proportional and integral (PI) control for driver's acceleration is added into the controller as RASMPIC for the stabilization of the proposed suspension system. In simulations, sinusoidal vibrations are used to test the controllers and the results show the RASMPIC has a better control performance to reduce driver's acceleration and improve the driving comfort than SMC and RASMC.

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Section: Introductionmentioning

confidence: 99%

Robust Adaptive Sliding Mode PI Control for Active Vehicle Seat Suspension Systems

Ning

2019

2019 Chinese Control and Decision Conference (CCDC)

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“…tarafından sentezlenmiştir [12]. Bozucu gözleyicisi tabanlı bir ön izlemeli kontrolör tasarımı ise Sever ve Yazıcı tarafından sunulmuştur [13]. [19][20][21].…”

Section: Gi̇ri̇ş (Introduction)unclassified

“…13) halinde yazılabilir. Burada A sistem matrisi, B1 bozucu giriş matrisi ve B 2 kontrol girişi için yazılan durum uzay matrisleridir.…”

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Biodinamik sürücü modeli içeren bir taşıt süspansiyon sisteminin durum türevi geri beslemeli LQR ile aktif titreşim kontrolü

Sever¹,

Şendur²,

Yazıcı³

et al. 2019

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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 LQR controller design with state derivative feedback  Convex optimization with Linear Matrix Inequalities (LMIs)  Integrated suspension, seat and driver dynamics In this study, the state-derivative feedback LQR design is proposed for the active vibration control of a vehicle suspension system. Controller gains are determined to minimize classical quadratic performance index as in conventional LQR method. However, proposed approach requires feedback of state derivative signals rather than state variables. Figure A. The closed loop block diagram obtained with state derivative feedback control law. Purpose: Main purpose of the study is to present practical synthesis method for controller design in active vibration control problems. In active vibration control problems, state derivative signals which are velocity and acceleration signals are more accurately obtained rather than displacement and velocity signals, since the accelerometers are mostly used. Therefore, state derivative feedback control has been employed instead of state feedback control which is frequently applied in the active suspension literature. Theory and Methods: Controller design is expressed as a semi-definite programming problem in convex optimization framework with linear matrix inequalities (LMIs). A five-degree-of-freedom vertical vibration model including suspension, seat and driver dynamics is used to examine controller performance in terms of ride comfort, safety and power consumption. Proposed controller and state feedback LQR structure have been examined for different road roughness grades and driving velocities by the use of ISO2631 standard. Results: Proposed controller and state feedback LQR structure have been examined with numerical simulations for different road roughness grades and driving velocities by the use of ISO2631 standard. It is shown that state derivative feedback controller provides nearly identical performance in terms of ride comfort and safety with more easily accessible feedback signals. Conclusion: Extending the method with robustness and gain scheduling capabilities might be a direction for further studies. Effect of the actuator dynamics and additional actuator in seat suspension can be investigated in future work.

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“…Keywords: Mathematical model, light car, passive suspension, neuro model, artificial road surface quite rational for representing the real suspension system dynamics [2,8]. On most cases, a quarter car model offen uses for analysis of ride and the controller design of suspension [1,[3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Neuro Model for Passive Suspension of a Light Car

Hanafi

Tee

Johana

et al. 2018

IJIE

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Disturbance Observer Based Optimal Controller Design for Active Suspension Systems

Cited by 13 publications

References 15 publications

Robust Adaptive Sliding Mode PI Control for Active Vehicle Seat Suspension Systems

Robust Adaptive Sliding Mode PI Control for Active Vehicle Seat Suspension Systems

Biodinamik sürücü modeli içeren bir taşıt süspansiyon sisteminin durum türevi geri beslemeli LQR ile aktif titreşim kontrolü

Neuro Model for Passive Suspension of a Light Car

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