Design concept for an alternative heavy vehicle ABS system

This paper summarises the measured emergency braking performance of a tri-axle heavy goods vehicle semitrailer fitted with a novel pneumatic slip control braking system developed by the Cambridge Vehicle Dynamics Consortium. Straight-line braking tests were carried out from 40 km/h in order to compare a commercially electro-pneumatic available anti-lock braking system and the Cambridge Vehicle Dynamics Consortium system, which has bi-stable valves coupled with a sliding-mode slip controller. On average, the Cambridge Vehicle Dynamics Consortium system reduced the stopping distance and the air use by 15% and 22% respectively compared with those for the conventional anti-lock braking system. The most significant improvements were seen on a wet basalt-tile surface (with similar friction properties to ice) where the stopping distance and the air use were improved by 17% and 30% respectively. A third performance metric, namely the mean absolute slip error, is introduced to quantify the ability of each braking system to track a wheel slip demand. Using this metric, the bi-stable valve system is shown to improve the wheel slip demand tracking by 62% compared with that of the conventional anti-lock braking system. This improvement potentially allows more accurate control of the wheel forces during extreme manoeuvres, providing scope for the future development of advanced stability control systems.

A laboratory Anti-Lock Brake System (ABS) is examined. The architecture of the ABS system is shown. The real-time experiments related to a control action to stabilize the slip at a certain level are taken into consideration. A new slip control algorithm differs from the previously used approaches. It is based on the measured current of the braking DC motor. The experimental results collected in the real-time for an old (relay) and new (current based) slip control are compared.

<div class="section abstract"><div class="htmlview paragraph">Anti-lock Braking System (ABS) is a critical safety component and its performance is crucial for every vehicle manufacturer. The tire plays an important role during an ABS braking maneuver as it is the component that connects the vehicle to the ground and is responsible for generating braking force. The steady-state and transient properties of the tire affect the operation of the vehicle's ABS system and consequently affects its performance/ operational efficiency. The main objective of this study is to investigate how tire design changes influence its interaction with the ABS and its eventual effect on stopping distance. This was conducted through an experimental study where tires were built with three levels of variation in carcass stiffness, tread stiffness and tread compound. Following this, ABS braking maneuvers were performed on two instrumented vehicles including a mid-tier sedan and a high-performance sports car. The steady-state properties of the tire were calculated from experimental data measured both on the vehicle and from the braking skid trailer and thereafter the sensitivity of a given ABS system to such changes in tire properties were analyzed. Lastly, the feasibility of adding intelligence in ABS controllers is investigated through an on-line tire identification routine to determine the tire slip set point value for the ABS controller to maximize the tire braking force.</div></div>

Vehicle electrification aims at improving energy efficiency and reducing pollutant emissions. It also creates opportunities to enhance active safety systems with the use of electric machines (EM) as regenerative braking actuators to support the friction brake system. Vehicle energy efficiency can be improved by integrating regenerative braking (RBS) and friction brakes to enable more frequent energy recuperation activations especially during high deceleration demands. This study aims at designing and implementing a new torque allocation strategy for wheel slip control in a four-wheel-drive (4WD) hybrid electric vehicle (HEV) with a single EM. The antilock braking system (ABS) is designed using sliding mode control, which offers robustness against tyre force nonlinearity. A brake torque blending algorithm is developed to distribute torques between electric and friction brake actuators based on a pre-defined energy recuperation priority. The challenge of allocating brake torques at each of the four wheels with brake-by-wire (BBW) and a centralized EM is particularly addressed in this study. Simulation analyses using a hardware-in-the-loop (HIL) test rig are conducted to verify the proposed control algorithms.

The anti-lock brake system (ABS) is an active safety device used in ground vehicles to increase the brake force between the tire and the road during panic braking. Due to the high non-linearity of the tire and road interaction plus uncertainties derived from vehicle dynamics, a standard proportional-integral-derivative (PID) controller is not deemed enough for the system to produce optimum performance. An active force control (AFC) based scheme is proposed to enhance the robustness of the system and reject undesirable disturbances. A P-type iterative learning algorithm (ILA) is implemented in the AFC loop to estimate the vital parameter continuously for force feedback compensation. In this paper, the control scheme to be known as PID-ILAFC was validated experimentally through its implementation on a test rig. A hardware-in-the-loop (HIL) test via LabVIEW was formulated with novel intelligent control schemes to execute the algorithm in real-time, thereby practically verifying the response of the ABS in the wake of parametric changes and varied operating and loading conditions. The PID-ILAFC controller is specifically designed to provide a proper slip ratio close to the reference value, a reduced stopping distance, and stability in vehicle movement during panic braking. The results clearly exhibit more robustness and superior performance of the AFC-based ABS in achieving the reduced stopping distance and good slip ratio in comparison to the PID and passive counterparts for a dry road condition setting.

<div class="section abstract"><div class="htmlview paragraph">An accurate estimate of vehicle speed is essential for optimal anti-lock braking system (ABS) calculations. Currently, most vehicles including heavy-duty class 8 trucks mainly rely on wheel speed sensors (WSS) to estimate velocity. However, as soon as braking is applied, WSS become inaccurate for determining the velocity due to the longitudinal slip developed in the tires. Using the inertial measurement unit (IMU) to estimate vehicle speed allows for its use in conjunction with the WSS to accurately calculate the slip ratio at each tire. These slip ratio values can then be used as the main control variable in the ABS algorithm to utilize the grip available more fully at each tire, to improve stopping distance and controllability. A steady state braking analysis model is developed and validated against Federal Motor Vehicle Safety Standards (FMVSS) 121 60-0 mph stopping distance data for a loaded class 8 tractor semi-trailer combination. Once the model is validated to show good correlation to measured deceleration data, it is used to estimate the theoretical peak steady state deceleration with the ideal slip ratio at each tire. Then a semi-empirical approach is used to consider current air brake system and ABS algorithm capabilities in the analysis. Lastly, the initial transient phase of 60-0 mph stop is accounted for to get a realistic stopping distance improvement estimate for a loaded heavy-duty truck.</div></div>

To explore the brake control strategy more suitable for heavy-duty multi-axle special vehicles, a comparative analysis of anti-lock brake system (ABS) control strategies based on heavy multi-axle special vehicles was carried out. Use Matlab/Simulink to build a heavy-duty single-wheel anti-lock control system including dynamics model, slip rate model, tire model, braking model and controller model. The current main control strategies are adopted: no ABS, logic Threshold control, PID control, sliding mode variable structure control, fuzzy PID control and nonlinear active disturbance rejection control are simulated and compared.The research results show that: (1) The comparison of anti-lock braking control strategies for heavy-duty multi-axle special vehicles can be achieved simply and quickly through Matlab/Simulink heavy-duty single-wheel parametric model simulation; (2) Several control strategies have obvious advantages and disadvantages , Needs to be selected according to actual needs; (3) Comprehensive control strategy characteristics and special vehicle requirements, nonlinear auto disturbance rejection control is more suitable for heavy-duty multi-axle special vehicle braking control.

Data-driven model-free slip control of anti-lock braking systems using reinforcement Q-learning

<div class="section abstract"><div class="htmlview paragraph">The electro-mechanical brake (EMB) system is a novel dry-type brake-by-wire system that features superior control performance and a compact structural design, effectively meeting the development demands of intelligent and electrified vehicles. However, current research on anti-lock braking system (ABS) primarily focuses on hydraulic brake system and mostly remains at the simulation and hardware-in-the-loop testing stages. Therefore, this paper validates the feasibility of slip ratio control based on EMB actuators through both simulation and real-vehicle experiments. First, this paper establishes an equivalent second-order response model for the closed-loop EMB control system through theoretical derivation and identifies the dynamic response characteristics of the EMB actuator via sinusoidal frequency sweep testing. Next, it compares two control strategies: one that uses the reference slip ratio as the direct control target, and another that uses reference wheel speed as the direct control target to indirectly regulate slip ratio. The latter effectively avoids the nonlinearities in slip ratio control caused by variations in vehicle speed. Based on reference wheel speed control, three types of slip ratio controllers were designed and derived: proportional-integral control (PI), integral sliding mode control (ISM), and super-twisting integral sliding mode control (STISM). Finally, simulation and real-vehicle tests on high-adhesion road surfaces verified that sliding mode slip ratio control based on reference wheel speed offers robustness, avoids the risks associated with overestimated controller gains, and improves the overall stability of the control system. In particular, the STISM, as a representative of high-order sliding mode control, effectively addresses the chattering issue present in traditional first-order sliding mode methods, offering enhanced braking safety and comfort.</div></div>

The rapid evolution of modern vehicles into intelligent and interconnected systems presents new complexities in both functional safety and cybersecurity. In this context, ensuring the reliability and integrity of critical sensor data, such as wheel speed inputs for anti-lock brake systems (ABS), is essential. Effective detection of wheel speed sensor faults not only improves functional safety, but also plays a vital role in keeping system resilience against potential cyber–physical threats. Although data-driven approaches have gained popularity for system development due to their ability to extract meaningful patterns from historical data, a major limitation is the lack of diverse and representative faulty datasets. This study proposes a novel dual learning model, based on Temporal Convolutional Networks (TCN), designed to accurately distinguish between normal and faulty wheel speed sensor behavior within a hardware-in-the-loop (HIL) simulation platform implemented on an FPGA. To address dataset limitations, a TruckSim–MATLAB/Simulink co-simulation environment is used to generate realistic datasets under normal operation and eight representative fault scenarios, yielding up to 5000 labeled sequences (balanced between normal and faulty behaviors) at a sampling rate of 60 Hz. Two TCN models are trained independently to learn normal and faulty dynamics, and fault decisions are made by comparing the reconstruction errors (MSE and MAE) of both models, thus avoiding manually tuned thresholds. On a test set of 1000 sequences (500 normal and 500 faulty) from the 5000 sample configuration, the proposed dual TCN framework achieves a detection accuracy of 97.8%, a precision of 96.5%, a recall of 98.2%, and an F1-score of 97.3%, outperforming a single TCN baseline, which achieves 91.4% accuracy and an 88.9% F1-score. The complete dual TCN architecture is implemented on a Xilinx ZCU102 FPGA evaluation kit (AMD, Santa Clara, CA, USA), while supporting real-time inference in the HIL loop. These results demonstrate that the proposed approach provides accurate, low-latency fault detection suitable for safety-critical ABS applications and contributes to improving both functional safety and cyber-resilience of braking systems.

Hierarchical [formula omitted] fuzzy-neural control of anti-lock braking system and active suspension in a vehicle

When vehicle speed increases, stable and powerful brake system is required to ensure the safety of the vehicle and passengers. For high speed braking of vehicle, contactless eddy current brake (ECB) is developed and applied to antilock brake system (ABS). An adaptive sliding mode controller for ABS using eddy current is presented with a sliding mode observer (SMO). Using vehicle acceleration, wheel speeds, and braking torque, the vehicle velocity estimated by SMO. To control the system based on wheel slip, half-car model is developed by considering uncertainty of the road slope and aerodynamic drag force. According to the controller, the vehicle ABS model is built with MATLAB Simulink. The response of controller is compared in terms of braking time and stopping distance. The results indicate proposed controller algorithm achieves minimum braking time and minimum stopping distance on selected roads.

This paper presents about the development of an Antilock Braking System (ABS) using quarter vehicle model and control the ABS using different type of controllers. Antilock braking system (ABS) is an important part in vehicle system to produce additional safety for drivers. In general, Antilock braking systems have been developed to reduce tendency for wheel lock and improve vehicle control during sudden braking especially on slippery road surfaces. In this paper, a variable structure controller has been designed to deal with the strong nonlinearity in the design of ABS controller. The controllers such as PID used as the inner loop controller and Fuzzy Logic as outer loop controller to develop as ABS model to control the stopping distance and longitudinal slip of the wheel.

This paper reports on the modeling of a pneumatic brake system and valve control strategies for commercial vehicles with an ABS (Anti-lock Brake System). A PCV (Pressure Control Valve), consisting of two solenoid valves and two diaphragm valves, is a key part of a pneumatic ABS. To examine the pneumatic ABS, a PCV model that could accurately represent the dynamics of the air flowing through the PCV components was developed. The dynamic compressible airflow equations, which can describe the flow through an orifice, were adopted to describe the directional motion of the dynamic airflow, as commanded by the driver and modified by the PCV to facilitate the ABS activity. The discharge coefficient as a function of the pressure ratio was obtained experimentally. The PCV model was verified through pneumatic measurements of the relay valve and brake chamber. The verification results demonstrated that the simulation data were in agreement with those obtained experimentally. Furthermore, the PCV model was integrated with a whole vehicle model in Trucksim to conduct ABS simulation studies. The investigated system involved both the continuous wheel dynamics and inherent discontinuous characteristics of the PCV Two on/off valve control strategies based on the pressure and switching surface were introduced to regulate the slip ratio and analyzed via simulations. In addition, the switching-surface-based control strategy was evaluated through a hardware-in-loop experimental setup. The experimental results demonstrated that the implemented control strategy is effective even under uncertainties in the pneumatic ABS.

This research presents a rule-based Anti-lock Braking System (ABS) algorithm towards active vehicle safety in Heavy Commercial Road Vehicles (HCRVs). Wheel Slip Regulation (WSR) algorithms, that are constituents of an ABS, are typically either model-based or rule-based. Model-Based Algorithms (MBAs) utilise mathematical models that characterise vehicle dynamics and are intensive in their demand for real-time information. On the other hand, Rule-Based Algorithms (RBAs) operate on set rules with pre-defined thresholds and utilise data from sensors that are typically available on the vehicle. A great deal of advancement has been reported in literature related to MBAs. However, most commercially available RBAs are proprietary in nature and the finer details are seldom revealed. Hence, this work proposes an RBA, which is a combined Slip and Wheel Acceleration Threshold Algorithm (SWATA), including a framework for identifying the importance of thresholds and their magnitudes. SWATA was tested on a Hardware-in-Loop (HiL) setup across varying road and loading conditions, and it provided a maximum of 36% braking distance improvement compared to a case where it was inactive. An adaptive version – ASWATA – is also proposed that can adapt the thresholds according to the particular tyre-road interface. Additionally, a quantitative comparison of the proposed RBA with a Sliding Mode Control (SMC) based MBA, which was developed and tested on the same HiL setup, is presented. It was observed that the performance of the RBA was on par with that of the MBA for most test cases, but with minimal data requirements.