Analysis Of Thresholds In Rule-Based Antilock Braking Control Algorithms

Challa, Akhil; Ramakrushnan, Karthik; Subramanian, Shankar C.; Vivekanandan, Gunasekaran; Sivaram, Sriram

doi:10.1016/j.ifacol.2020.06.068

Cited by 5 publications

(4 citation statements)

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“…Additional wheel acceleration thresholds, a lb and a ub , were utilised to prevent wheel lock. 28 The two variables, l and a, and their corresponding thresholds are depicted in Figure 4. 28 At each time instant, the 'Phase Identification' block in The desired operating phase (apply/hold/exhaust) is converted to desired brake pressure for implementation using the brake controller, depicted as the 'Phase to Pressure' block in Figure 3.…”

Section: An Overview Of 3-phase Wheel Slip Regulation Algorithmmentioning

confidence: 99%

“…28 The two variables, l and a, and their corresponding thresholds are depicted in Figure 4. 28 At each time instant, the 'Phase Identification' block in The desired operating phase (apply/hold/exhaust) is converted to desired brake pressure for implementation using the brake controller, depicted as the 'Phase to Pressure' block in Figure 3. In the apply-phase, the reservoir pressure is scaled by the EPR gain, calculated by the 'EPR Gain Calculator' block in Figure 3, and is sent to the brake controller.…”

Section: An Overview Of 3-phase Wheel Slip Regulation Algorithmmentioning

confidence: 99%

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An anti-lock braking system algorithm using real-time wheel reference slip estimation and control

Gaurkar

Ramakrushnan

Challa

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Knowledge of the tyre-road interface traction limit during braking of a road vehicle can drastically improve safety and ensure stable braking on varied road conditions. This study proposes an optimal reference slip algorithm that determines the road surface while the vehicle is braking, by implicitly tracking the traction limit. It presents wheel slip variance regulation as a potential approach towards reference wheel slip estimation for wheel slip regulation (WSR). The variance regulation approach computes reference wheel slip using past wheel slip estimates and regulates wheel slip variation at a set point. This variance regulation problem was solved using least-squares estimation, yielding reference slip dynamics. A 3-staged nested control architecture was developed with reference slip dynamics to yield an anti-lock braking system (ABS) algorithm consisting of a brake controller, WSR algorithm and reference slip estimation. The algorithm was experimentally corroborated in a Hardware-in-Loop setup consisting of the pneumatic brake system of a heavy commercial road vehicle, and IPG TruckMaker®, a vehicle dynamics simulation software. The proposed ABS algorithm was tested on straight roads with homogeneous surfaces, split friction surfaces, and transition friction surfaces. It ensured stable braking in all road cases, with a 7%–18% reduction in braking distance on homogeneous road surfaces compared to the same vehicle without ABS. The vehicle directional stability was retained on a split-friction surface, and the ABS algorithm was observed to adapt to sudden transitions in the road surface.

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Section: An Overview Of 3-phase Wheel Slip Regulation Algorithmmentioning

confidence: 99%

Section: An Overview Of 3-phase Wheel Slip Regulation Algorithmmentioning

confidence: 99%

An anti-lock braking system algorithm using real-time wheel reference slip estimation and control

Gaurkar

Ramakrushnan

Challa

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to improve the control effect of the ACC system in actual driving scenarios, it is necessary to consider the influence of road surface adhesion changes on the performance of the braking system when designing the control method at the execution layer [ 28 , 29 ]. Therefore, a rule-based anti-lock braking system (ABS) control method based on the execution layer is proposed.…”

Section: Acc System Control Strategy Developmentmentioning

confidence: 99%

“…By optimizing control variables and prediction errors while accounting for various performance constraints and evaluation indicators, the ACC system can more effectively track target vehicles. The objective function is displayed in Equation (28).…”

Section: Constraint Namementioning

confidence: 99%

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

Guo

Wang

Liang

et al. 2023

Sensors

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Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. Firstly, a deep learning-based dynamic normal wheel load observer is added to the perception layer of the conventional ACC system and the observer output is used as a prerequisite for brake torque allocation. Secondly, a Fuzzy Model Predictive Control (fuzzy-MPC) method is adopted in the ACC system controller design, which establishes performance indicators, including tracking performance and comfort, as objective functions, dynamically adjusts their weights and determines constraint conditions based on safety indicators to adapt to continuously changing driving scenarios. Finally, the executive controller adopts the integral-separate PID method to follow the vehicle’s longitudinal motion commands, thus improving the system’s response speed and execution accuracy. A rule-based ABS control method was also developed to further improve the driving safety of vehicles under different road conditions. The proposed strategy has been simulated and validated in different typical driving scenarios and the results show that the proposed method provides better tracking accuracy and stability than traditional techniques.

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