Sliding-mode control for semi-active suspension with actuator dynamics

Chen, Bo–Chiuan; Shiu, Yu-Hua; Hsieh, Feng-Chi

doi:10.1080/00423111003602376

Cited by 48 publications

(21 citation statements)

References 9 publications

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“…Recently, automobile ASS has become an effective and popular way to deal with this big trade-off between the inherent conflicting suspension performances [2][3]. For vehicle ASS under normal working operation, a number of control algorithms, such as backstepping control [4][5], ∞ control [6][7], sliding mode control [8][9], neural network control [10][11], etc., have been proposed to achieve the improvement of vehicle dynamics performances.…”

Section: Introductionmentioning

confidence: 99%

Design of a Sliding Mode Observer-Based Fault Tolerant Controller for Automobile Active Suspensions With Parameter Uncertainties and Sensor Faults

2020

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This paper presents a sliding mode observer-based fault tolerant controller for a class of active suspension with the parametric uncertainties and sensor faults. First, T-S fuzzy approach is employed to represent the inertial parameters uncertainties and sensor faults, and then an augmented vehicle dynamics model is established. To estimate both system state variables and sensor fault signal simultaneously, a sliding-mode observer is investigated, and the fault tolerant control law is further derived in terms of the estimation of state and state error within Lyapunov theory framework, moreover, the sufficient conditions for the existence of this controller is deduced and solved via a set of linear matrix inequalities. Finally, a complete comparative simulation case is provided to demonstrate the effectiveness and feasibility of the designed control method. INDEX TERMS active suspension system, sliding mode observer, fault tolerant control, T-S fuzzy approach. Nomenclature mc Vehicle body mass [kg] ϕ Pitch angle of vehicle body [rad] Iy Moment of inertia in vehicle body [kg• m² ] cf Damping coefficient of front suspension [N•s•m-1 ] muf Mass of the front suspension wheel [kg] cr Damping coefficient of rear suspension [N•s•m-1 ] mur Mass of the rear suspension wheel [kg] ktf Stiffness of the front tire [N•m-1 ] a Distance from COG to the front axle [m] ktr Stiffness of the rear tire [N•m-1 ] b Distance from COG to the rear axle [m] uf Front actuator force [F] zuf Vertical displacement of the front wheel [m] ur Rear actuator force [F] zur Vertical displacement of the rear wheel [m] ∆yf Front suspension dynamic travel [m] zrf Road excitations at the front wheel [m] ∆yr Rear suspension dynamic travel [m] zrr Road excitations at the rear wheel [m] kf Stiffness coefficient of the front suspension [N•m-1 ] zc Vertical displacement of vehicle body [m] kr Stiffness coefficient of the rear suspension [N•m-1 ]

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Section: Introductionmentioning

confidence: 99%

Design of a Sliding Mode Observer-Based Fault Tolerant Controller for Automobile Active Suspensions With Parameter Uncertainties and Sensor Faults

2020

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“…10 The application of GPS system into MR/pneumatic suspension control is proposed by Morales, achieved the same roll angle levels as in a comparable passive vehicle while improving ride comfort by reducing accelerations up to 30%. 11 The control strategies commonly used for semi-active suspension include optimal control, 10,[12][13][14] skyhook control, 15,16 sliding mode control, 17,18 fuzzy control, 19,20 and PID control, 21,22 etc. Skyhook control and sliding mode control exhibit certain robustness, but they are not designed to realize the optimal system performance.…”

Section: Introductionmentioning

confidence: 99%

Investigation on linear quadratic Gaussian control of semi-active suspension for three-axle vehicle

Zhao

Zhang

2020

Journal of Low Frequency Noise, Vibration and Active Control

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An 8-DOF three-axle vehicle model with semi-active suspension is built in this paper, of which the accuracy is verified through simulations and experiments. Based on the optimal control theory, the linear quadratic Gaussian controller for semi-active suspension is designed with 10 evaluation indicators. Considering the deficiency of linear quadratic Gaussian control weight coefficients based on experience, analytic hierarchy process is employed to determine the weight coefficients of each indicator. The control effect is analyzed through MATLAB/Simulink. The adaptability of proposed control strategy under 25 driving conditions is analyzed with different road grades and speeds. The driving condition of “70 km/h travel speed on the road of grade B” is selected, under which the comparison of vehicle responses between semi-active suspension and passive suspension is made. Results show that the vertical vibration is effectively diminished by using semi-active suspension with linear quadratic Gaussian controller. Compared with passive suspension, the riding comfort is improved and the adverse effect on handling stability is eliminated. The three-axle vehicle with semi-active suspension has good adaptability to various working conditions.

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“…[26][27][28] Therefore, the sliding mode controller is selected as the controller of the vehicle suspension and TMD for its excellent stability and outstanding performance with two or more control objectives. 22,24,29 As a result, the proposed structure can eliminate the unsprung adverse effect totally as well as improve the ride comfort across the whole frequency spectrum. In addition, the hardware-in-the-loop simulation indicates that the designed controller also behaves well in the presence of nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

A method to eliminate unsprung adverse effect of in-wheel motor-driven vehicles

Nie

Zhuang

Chen³

et al. 2018

Journal of Low Frequency Noise, Vibration and Active Control

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In-wheel motor-driven vehicles are the development trend for future vehicles due to its high energy efficiency and low emission as well as its flexibility to achieve independent steering, driving, etc. However, the weighted wheel of in-wheel electric vehicles involves more unexpected unsprung vibrations, which imposes adverse effect on vehicle ride comfort. In addition, there exists an invariant point around the unsprung resonance frequency in both controlled and uncontrolled suspensions, which greatly limits the elimination of unsprung adverse effect of in-wheel electric vehicles. In this paper, a combined structure is proposed to eliminate the unsprung adverse effect. The structure is composed of the vehicle suspension and a tuned mass damper, which are both controlled by a sliding mode controller, aiming at eliminating the unsprung adverse effect as well as improving ride comfort across the whole frequency spectrum. The tunes mass damper is used to get rid of the constraint of the invariant point. The simulation and hardware-in-theloop results show that the root mean square of the sprung mass acceleration and tire deflection is reduced by 31.2% and 2.2% respectively, which indicates that the proposed method is effective and ride comfort is greatly improved.

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Sliding-mode control for semi-active suspension with actuator dynamics

Cited by 48 publications

References 9 publications

Design of a Sliding Mode Observer-Based Fault Tolerant Controller for Automobile Active Suspensions With Parameter Uncertainties and Sensor Faults

Design of a Sliding Mode Observer-Based Fault Tolerant Controller for Automobile Active Suspensions With Parameter Uncertainties and Sensor Faults

Investigation on linear quadratic Gaussian control of semi-active suspension for three-axle vehicle

A method to eliminate unsprung adverse effect of in-wheel motor-driven vehicles

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