The road adhesion coefficient is a key factor influencing automatic emergency braking (AEB) and anti-lock braking system (ABS) safety control of trucks. With the fading factor introduced, and the covariance gain adjusted in real time, the strong tracking unscented Kalman filter (STUKF) algorithm is modified to estimate the road adhesion coefficient more accurately. Composed of an ABS fuzzy sliding mode controller (SMC) and an AEB controller, an AEB/ABS coordinated control strategy with an adhesion coefficient estimation is designed for a three-axle heavy vehicle. The control effects are verified through experiments on various road conditions based on a hardware-in-loop test platform. The test results show that the proposed control strategy has a better braking efficiency than the traditional AEB/ABS and AEB control strategy without adhesion coefficient estimation and can decrease braking distance by 8.4% and braking time by 5.9%, which is beneficial to the vehicle longitudinal safety.
The road friction coefficient and the forces between the tire and the road have a significant impact on the stability and precise control of the vehicle. For four-wheel independent drive electric vehicles, an adaptive tire force calculation method based on the improved Levenberg–Marquarelt multi-module and self-organizing feedforward neural networks (LM-MMSOFNN) was proposed to estimate the three-directional tire forces of four wheels. The input data was provided by common sensors amounted on the autonomous vehicle, including the inertial measurement unit (IMU) and the wheel speed/rotation angle sensors (WSS, WAS). The road type was recognized through the road friction coefficient based on the vehicle dynamics model and Dugoff tire model, and then the tire force was calculated by the neural network. The computational complexity and storage space of the system were also reduced by the improved LM learning algorithm and self-organizing neurons. The estimation accuracy was further improved by using the Extended Kalman Filter (EKF) and Moving Average (MA). The performance of the proposed LM-MMSOFNN was verified through simulations and experiments. The results confirmed that the proposed method was capable of extracting important information from the sensors to estimate three-directional tire forces and accurately adapt to different road surfaces.
Accurate and efficient road adhesion coefficient estimation is the premise for the proper functioning of vehicle active safety control system. With the increased application of distributed drive vehicles and on-board sensors, a multi-module self-organizing feedforward neural network (LM-MMSOFNN) based on improved Levenberg-Marquardt (LM) learning algorithm is proposed for online road adhesion coefficient estimation. In this method, the vehicle dynamics model and the Dugoff tire model were well established, and the input and output variables of the neural network model were obtained by Principal Component Analysis (PCA) method. To improve the estimation accuracy, Extended Kalman Filter (EKF) and Moving Average (MA) were used to denoise the measured signal. On this basis, a road adhesion coefficient estimation model based on multi-module self-organizing neural network was established. Both sides of road adhesion coefficients are calculated by multi-module self-organizing neural network simultaneously. Through the increase and decrease of self-organizing neurons and the improved LM learning algorithm, the computational complexity and system hardware storage are reduced, and the algorithm exhibits a good adaptability to different roads. Simulation and vehicle experiments show that the proposed method can fully extract multi-sensor information and adapt to the different road characteristics changes under driving condition. As compared with Kmeans method, it has higher estimation accuracy and stronger adaptability to varying speed.
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