Observer-Based State Feedback Control for Vehicle Chassis Stability in Critical Situations

Dahmani, H.; Pagès, O.

doi:10.1109/tcst.2015.2438191

Cited by 29 publications

(22 citation statements)

References 20 publications

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“…where a i , b i , c i , C fi , and C ri (i = 1, 2) are identified using the Levenberg-Marquardt algorithm [25]. More details are given in Table 2.…”

Section: B Representation For T-s Full-car Vehicle Modelmentioning

confidence: 99%

Fuzzy Observer-Based Prescribed Performance Control of Vehicle Roll Behavior via Controllable Damper

Wang

Qin

et al. 2019

IEEE Access

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This paper presents a novel observer-based control strategy to improve the vehicle roll behavior performance through magneto-rheological (MR) dampers under steering wheel input and various road excitation conditions. Since the vehicle roll with sudden steering input is an essential part of driving safety and possesses inherent nonlinearities, the full-car nonlinear Takagi-Sugeno (T-S) fuzzy model is first established to describe the vehicle roll dynamics considering nonlinear coupling dynamics of tire lateral force and MR damper force under road excitation input. Furthermore, a T-S model-based fuzzy observer is adapted to estimate the vehicle roll angle and roll rate. The stability conditions for the used T-S observer are calculated using linear matrix inequalities (LMIs), and the proposed observer is induced by solving the proposed LMI. Based on the Lyapunov function, sliding mode theory and prescribed performance function, a novel state observer-based prescribed performance control strategy is developed to constrain the controlled vehicle roll angle and roll rate state within the prescribed performance boundaries. Finally, the proposed techniques are validated through the J-turn and Fishhook tests conducted via a high-fidelity CarSim software platform. INDEX TERMS Vehicle roll dynamics, prescribed performance control, Takagi-Sugeno fuzzy observer, state estimation, vehicle system.

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“…where a i , b i , c i , C fi , and C ri (i = 1, 2) are identified using the Levenberg-Marquardt algorithm [25]. More details are given in Table 2.…”

Section: B Representation For T-s Full-car Vehicle Modelmentioning

confidence: 99%

Fuzzy Observer-Based Prescribed Performance Control of Vehicle Roll Behavior via Controllable Damper

Wang

Qin

et al. 2019

IEEE Access

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“…C fi , C ri are the front and rear tire cornering stiffness which depend of road adhesion and vehicle mass m v , f (t) and r (t) are slip angles of front and rear tires, respectively. Which are given in [5] as follows:…”

Section: T-s Fuzzy Vehicle Model and Problem Formulationmentioning

confidence: 99%

“…In the event of a deviation in an undesirable direction, the computer makes immediate decisions to react with corrective actions to ensure the stability of the vehicle. The influence of the vehicle speed and the variation of road adhesion is presented in the works [4,5]. The control based on the estimated state feedback is widely studied in recent years as in [7,8], The authors have proposed an approach to stabilize the system of vehicle dynamics in the presence of disturbances.…”

Section: Introductionmentioning

confidence: 99%

Static output-feedback $$H_\infty$$ control for T–S fuzzy vehicle lateral dynamics

et al. 2019

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This paper deals with static output-feedback H ∞ control for a system of the vehicle lateral dynamics, represented by Takagi-Sugeno (T-S) fuzzy models. Sufficient conditions of the existence of H ∞ control based on the static output-feedback are presented. The bilinear matrix inequalities are converted to a set of linear matrix inequalities, with the aid of some special derivations. Simulation results demonstrate the effectiveness of the proposed method.

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“…For simulation and control design was used a model that considers lateral, yaw and roll motions with simplifications for constant longitudinal speed [2,12,15,25]. Figure 1 shows this model represented by the following equations of motions:…”

Section: Vehicle Dynamicmentioning

confidence: 99%

Development and evaluation with MIL and HIL simulations of a LQR-based upper-level electronic stability control

Magalhães

Murilo

Lopes

2019

J Braz. Soc. Mech. Sci. Eng.

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This paper proposes an electronic stability control and shows MIL and HIL simulations performed for evaluation. Two designs are presented, one based on two-degrees-of-freedom linear model that includes side-slip and yaw motion and other based on three-degrees-of-freedom linear model that includes roll motion. Model-in-the-loop and Hardware-in-the-loop simulations were performed to test ESC on vehicle represented by a nonlinear model that considers lateral, yaw and roll motions and an intelligent driver model to generate the steering wheel command as driver reaction to vehicle motion in respect with desired path. It's found from simulations for double lane change maneuver that the proposed controller is effective in reducing of sideslipping, rolling and yaw rate errors, keeping the steering stable in scenarios where the driver loses control of the vehicle, even in presence of disturbances in vehicle response in relation with response predicted by linear model used for control design. Keywords Electronic stability control • Linear quadratic regulator • Hardware-in-the-loop • Vehicle steering dynamic List of symbols a Length from front axis to center of mass b Length from rear axis to center of mass l Vehicle total length from rear axis to front axis t f Front track width t r Rear track width h s Height of center of mass above rolling center h Height of center of mass m s Sprung mass I zz Yawing inertial moment I xx Rolling inertial moment I xz Inertial product related to yawing and rolling c f Front rolling damping coefficient c r Rear rolling damping coefficient k f Front rolling stiffness coefficient k r Rear rolling stiffness coefficient f ∕ Front steer-by-roll coefficient M u Extra yaw moment given as control signal I s Steering coefficient u Vehicle longitudinal speed v Vehicle lateral speed Yaw angle Roll angle f Steering angle of front wheels r Steering angle of rear wheels D Front steer angle due to the driver's steering wheel command F yi Lateral force generated by tire i Camber angle i Tire slip angle of wheel i Vehicle side-slip angle g Gravity acceleration C i Cornering stiffness of wheel i C i Camber stiffness K Camber gradient r ∕ Rear steer-by-roll coefficient Tire-road friction coefficient

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Observer-Based State Feedback Control for Vehicle Chassis Stability in Critical Situations

Cited by 29 publications

References 20 publications

Fuzzy Observer-Based Prescribed Performance Control of Vehicle Roll Behavior via Controllable Damper

Fuzzy Observer-Based Prescribed Performance Control of Vehicle Roll Behavior via Controllable Damper

Static output-feedback $$H_\infty$$ control for T–S fuzzy vehicle lateral dynamics

Development and evaluation with MIL and HIL simulations of a LQR-based upper-level electronic stability control

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