Real-time estimation of the pressure in the wheel cylinder with a hydraulic control unit in the vehicle braking control system based on the extended Kalman filter

Jiang, Guirong; Miao, Xiaolu; Wang, Yuehui; Chen, Jie; Li, Dalong; Liu, Lifu; Muhammad, Fahad

doi:10.1177/0954407016671685

Cited by 19 publications

(8 citation statements)

References 19 publications

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“…A recursive least-squares approach was used in [21] to estimate brake pressure by using characteristics of the pressure response of antilock braking systems. An Extended Kalman Filter based approach, combining tire dynamics and hydraulic models, was used in [22]. Other approaches have included the design of inverse models for brake pressure [23], modeling the decrease, increase, and hold of brake pressure using experimental data [24], including measuring the amount of fluid passing through the valve to determine brake pressure [25].…”

Section: Introductionmentioning

confidence: 99%

A sensorless state estimation for a safety-oriented cyber-physical system in urban driving: Deep learning approach

Al-Sharman

Murdoch

Cao

et al. 2021

IEEE/CAA J. Autom. Sinica

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In today's modern electric Vehicles, enhancing the Safety-Critical Cyber-Physical (CPS) system's performance is necessary for the safe maneuverability of the vehicle. As a typical CPS system, the braking system is crucial for the vehicle design and safe control. However, precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy. In this paper, a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach. A Deep Neural Network (DNN) is structured and trained using special deep-learning training techniques, such as, dropout and rectified units. These techniques are utilized to obtain more accurate model for brake pressure state estimation applications. The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing. The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles. Based on these experimental data, the DNN is trained and the performance of the proposed state estimation approach is validated accordingly. The results demonstrate highaccuracy brake pressure state estimation with RMSE of 0.048 MPa.

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Section: Introductionmentioning

confidence: 99%

A sensorless state estimation for a safety-oriented cyber-physical system in urban driving: Deep learning approach

Al-Sharman

Murdoch

Cao

et al. 2021

IEEE/CAA J. Autom. Sinica

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“…The Kalman filter is a popular estimation method with a low computational cost [14][15][16][17]. As updated versions of a Kalman filter, an EKF [18][19][20][21][22] and a DEKF [23,24] have already been proven to be accurate and suitable for estimation in nonlinear vehicle dynamic models. However, for a four-axle truck, the rotational inertia and the CG position may vary within a large range, and the complexity of the multi-axle setup mean that there are more unknown parameters and states to estimate.…”

Section: Introductionmentioning

confidence: 99%

Vertical Tire Forces Estimation of Multi-Axle Trucks Based on an Adaptive Treble Extend Kalman Filter

Zhang

Wang

et al. 2021

Chin. J. Mech. Eng.

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Vertical tire forces are essential for vehicle modelling and dynamic control. However, an evaluation of the vertical tire forces on a multi-axle truck is difficult to accomplish. The current methods require a large amount of experimental data and many sensors owing to the wide variation of the parameters and the over-constraint. To simplify the design process and reduce the demand of the sensors, this paper presents a practical approach to estimating the vertical tire forces of a multi-axle truck for dynamic control. The estimation system is based on a novel vertical force model and a proposed adaptive treble extend Kalman filter (ATEKF). To adapt to the widely varying parameters, a sliding mode update is designed to make the ATEKF adaptive, and together with the use of an initial setting update and a vertical tire force adjustment, the overall system becomes more robust. In particular, the model aims to eliminate the effects of the over-constraint and the uneven weight distribution. The results show that the ATEKF method achieves an excellent performance in a vertical force evaluation, and its performance is better than that of the treble extend Kalman filter.

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“…In [21], a recursive least square algorithm for estimation of brake cylinder pressure was proposed based on the pressure response characteristics of anti-lock braking system (ABS). In [22], an extended-kalman-filter-based estimation algorithm was developed considering hydraulic model and tyre dynamics. In [23], an algorithm for online observation of brake pressure was designed through a developed inverse model, and the algorithm was verified in the vehicle's electronic stability program.…”

Section: Introductionmentioning

confidence: 99%

Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System

Zhang

et al. 2018

IEEE Trans. Ind. Inf.

294

110

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As an important safety critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer Artificial Neural Networks (ANN) with Levenberg-Marquardt Backpropagation (LMBP) training algorithm. Firstly, the highlevel architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feedforward neural network (FFNN) is introduced. Based on the basic concept of backpropagation, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.

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Real-time estimation of the pressure in the wheel cylinder with a hydraulic control unit in the vehicle braking control system based on the extended Kalman filter

Cited by 19 publications

References 19 publications

A sensorless state estimation for a safety-oriented cyber-physical system in urban driving: Deep learning approach

A sensorless state estimation for a safety-oriented cyber-physical system in urban driving: Deep learning approach

Vertical Tire Forces Estimation of Multi-Axle Trucks Based on an Adaptive Treble Extend Kalman Filter

Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System

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