Vehicle parameter estimation and stability enhancement using sliding modes techniques

Shraim, Hassan; Ouladsine, Mustapha; Romero, Mónica E.

doi:10.1504/ijvd.2008.022578

Cited by 23 publications

(11 citation statements)

References 34 publications

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“…Various estimation schemes are available for longitudinal and lateral speed estimation that use extended Kalman filter [17], adaptive Kalman filter [18], and Sliding Mode Observers (SMO) [19]. Although the performance of the above methods is similar, Kalman filter based estimation schemes require a large amount of computation [20]. But SMO avoids massive matrix computation and gives better parameter robustness and is more practically feasible than the Kalman filter [21].…”

Section: Introductionmentioning

confidence: 99%

Brake Fault Identification and Fault-Tolerant Directional Stability Control of Heavy Road Vehicles

2020

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Accurate fault diagnosis in air brake is crucial to reduce frequent brake inspection and maintenance in heavy commercial road vehicles. Existing model-based fault diagnostic schemes work well under limited vehicle operating conditions, which is insufficient for developing an on-board monitoring device. In this context, a learning-based fault identification scheme using the Random Forest technique, which accommodates the vehicle's wide operating conditions, is proposed. This scheme identifies the brake's fault levels with a better classification accuracy of 92% compared to techniques such as Naïve Bayes, k-Nearest Neighbors, Support Vector Machine, and Decision Tree. Further, a fault-tolerant controller is proposed to overcome the vehicle's directional instability arising due to the brake fault. Two sliding mode controllers, namely differential brake control and steering angle control, were developed to control the yaw angle. These have been implemented in a Hardware in Loop experimental platform with the vehicle dynamic simulation software TruckMaker ® .

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Section: Introductionmentioning

confidence: 99%

Brake Fault Identification and Fault-Tolerant Directional Stability Control of Heavy Road Vehicles

2020

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“…10 Grip et al 11 use a non-linear observer based on asymptotic stabilization of estimation errors guaranteed by means of Lyapunov functions. Other approaches are those of Shraim et al 12 and Cadiou et al 13 who use sliding mode observers and Zhao et al 14 who use moving horizon strategies. With all these methods, estimation is strongly influenced by the vehicle and tire models and system uncertainties.…”

Section: Introductionmentioning

confidence: 99%

An integrated artificial neural network–unscented Kalman filter vehicle sideslip angle estimation based on inertial measurement unit measurements

Novi

Capitani

Annicchiarico³

2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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Vehicle dynamics stability control systems rely on the amount of so-called sideslip angle and yaw rate. As the sideslip angle can be measured directly only with very expensive sensors, its estimation has been widely studied in the literature. Because of the large non-linearities and uncertainties in the dynamics, model-based methods are not a good solution to estimate the sideslip angle. On the contrary, machine learning techniques require large datasets that cover the entire working range for a correct estimation. In this paper, we propose an integrated artificial neural network and unscented Kalman filter observer using only inertial measurement unit measurements, which can work as a standalone sensor. The artificial neural network is trained solely with numerical data obtained with a Vi-Grade model and outputs a pseudo-sideslip angle which is used as input for the unscented Kalman filter. This is based on a kinematic model making the filter completely transparent to model uncertainty. A direct integration with integral damping and integral reset value allows the estimation of the longitudinal velocity of the kinematic model. A modification strategy of the pseudo-sideslip angle is then proposed to improve the convergence of the filter’s output. The algorithm is tested on both numerical data and experimental data. The results show the effectiveness of the solution.

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“…In [3], the SMC theory is applied to control vehicle manoeuvering by constructing a sliding mode (SM) controller, which shows good robustness under various driving conditions including changes in velocity, road friction and vehicle weight. Meanwhile, the SM observers are also used in [4] for identifying and estimating tyre's longitudinal force, vehicle sideslip angle and velocity. Based on the observed values, a SM controller is further developed for the sake of driving the yaw rate, the sideslip angle and the velocity to their reference signals.…”

Section: Introductionmentioning

confidence: 99%

Direct yaw-moment control of in-wheel electric vehicle by sliding mode technique

Zhang

Ding

Jiang

2016

2016 IEEE International Conference on Industrial Technology (ICIT)

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In the paper, a direct yaw-moment control (DYC) strategy is proposed for in-wheel electric vehicles by using second-order sliding mode control technique. The ideal sideslip angle at the center of gravity and yaw rate are first calculated based on a linear two-degree of freedom vehicle model. On this basis, the actual sideslip angle is identified and estimated by constructing a state observer. Then a conventional discontinuous sliding mode DYC controller is first constructed to guarantee that the sideslip angle and yaw rate will approach the ideal ones as closely as possible. To avoid the chattering problem existing in the conventional sliding mode controller, a secondorder sliding mode controller is further designed by taking the derivative of the controller as the new control, which implies that the actual control can be the integration of the second-order sliding mode controller. Simulation results verify the effectiveness of the proposed method.Index Terms-Sliding mode, direct yaw-moment control, inwheel electric vehicle.

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Vehicle parameter estimation and stability enhancement using sliding modes techniques

Cited by 23 publications

References 34 publications

Brake Fault Identification and Fault-Tolerant Directional Stability Control of Heavy Road Vehicles

Brake Fault Identification and Fault-Tolerant Directional Stability Control of Heavy Road Vehicles

An integrated artificial neural network–unscented Kalman filter vehicle sideslip angle estimation based on inertial measurement unit measurements

Direct yaw-moment control of in-wheel electric vehicle by sliding mode technique

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