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Anti-lock braking system (ABS) prevents the wheels from being locked in hard braking conditions and reduces the vehicle stopping distance to the minimum value by regulating the tire longitudinal slip at its optimum value. This paper presents a two-layer controller for ABS of trucks which is adaptable with different road conditions. In the upper layer, a fuzzy controller is designed to calculate the optimum longitudinal slip of each wheel for which the maximum braking force is achieved in different conditions. In the lower layer, a nonlinear controller is analytically designed based on the predictive method to track the optimum wheel slips calculated from the upper layer. In order to increase the robustness of the controller in the presence of system uncertainties, the integral feedback technique is also appended to the predictive method. All simulation studies are conducted using the professional software of Truck Sim to evaluate the performance of the controlled system in a real condition. The results show the effectiveness of the proposed control system in improving the braking performance of trucks in different road conditions.

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The anti-lock braking system is one of the main factors to make safety in designing vehicles. The brake pressure control and desired slip tracking through severe braking cause safety in vehicles. Because of uncertainty in parameters and sever nonlinear factors, robust controller designing is suitable for this system. In this paper the types of sliding mode controller have been used to achieve a vehicle desired slip and its stop. Sliding surface and terminal attractions will be analyzed in all of the designed controllers. Also a new structure with high terminal attraction have been for the fast terminal sliding mode controller (FTSMC). The proposed method has reduced tracking error as well. In this paper, the performance of this controller is compared with normal terminal sliding mode controller and fast terminal sliding mode. Also all design parameters are determined to decrease error ratio using particle swarm optimization (PSO) algorithm. This method is suitable for solving complex optimized solution based on certain cost function. Simulation results with using MATLAB software, present better performance of the suggested controller in comparison with normal and fast terminal sliding mode controller.

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In recent decades, the evolution and advancement of automotive technology have played a crucial role in enhancing the safety and security of drivers and passengers. One prominent technology that significantly contributes to vehicle safety is the Anti-lock Braking System (ABS), which notably improves safety during braking, reduces braking distances, and enhances vehicle control across various road surface conditions. This paper introduces an innovative real-time algorithm aimed at improving the performance of ABS systems, leveraging practical data instead of complex mathematical modeling. Unlike traditional modeling approaches that rely on half car model and dynamic tire simulations, this study utilizes actual wheel data to develop and optimize the algorithms. This approach enhances the sensitivity and adaptability of the algorithm to real-world road changes and varying operational conditions. Furthermore, the method effectively addresses and analyzes the phenomena of Jump and Split, which have not been specifically tackled in other studies. Validated through both analytical and empirical models, this approach provides high accuracy in estimating acceleration in scenarios where accelerometer sensors are unavailable. It offers precise responses under varying braking conditions. Simulation results of the algorithm using practical test data indicate that it achieves approximately 49.1% faster detection times compared to other algorithms, offering significant advantages such as reduced braking distances and lower computational costs.