Braking Control Research Articles

Heavy-Duty Vehicle Braking Stability Control and HIL Verification for Improving Traffic Safety

The braking failure of heavy vehicles under long downhill or curved conditions may cause traffic crash and reduce road traffic efficiency. Therefore, to improve the traffic safety and braking stability of vehicles under special road conditions, a braking dynamic model and control system based on the interval uncertainty analysis are proposed, and the safety of the active control model is verified by experiments (HIL). Firstly, the interval uncertain dynamic model is established based on the Monte Carlo method, and the braking failure simulation analysis of the right front wheel of heavy vehicles is carried out in the set of three uncertain intervals. Secondly, the fuzzy PID and sliding mode controller based on yaw and centroid error are designed to find the optimal control strategy from the two kinds of control strategies for HIL experiments. Finally, the actual control effect and feasibility of these control algorithms for heavy vehicle braking under special road conditions are verified by HIL experiments. The experimental results show that under the action of the fuzzy PID control strategy, the running stability of the vehicle is significantly improved compared with no control, which effectively reduces the risk of vehicle braking failure and improves the active safety and stability of the vehicle.

A Study on Automatic Emergency Braking Control Algorithm Based on Professional Drivers’ Braking Behavior

A Study on Automatic Emergency Braking Control Algorithm Based on Professional Drivers’ Braking Behavior

Heavy-load train model based on RBF neural network refers to the adaptive brake control strategy

Heavy-load train braking control is an important automatic driving function. The braking mode mainly used in China is air braking, which has great nonlinear characteristics. In order to reduce the influence of system uncertainty on control, improve the control accuracy and ensure the safety and efficiency of train transportation, an adaptive brake control strategy is designed.Firstly, establish the state space model of braking system according to the dynamic process of overloaded train braking, use the adaptive method and design the RBF model to track the train speed. Finally, the actual data from CK1466.1 to CK1431.3 in the line is used as the reference model. Experimental results The RBF neural network model designed by the reference adaptive controller realizes the tracking control of a given speed curve with high accuracy, verifying the effectiveness of the strategy.

Nonlinear Control Design for Regenerative and Hybrid Antilock Braking in Electric Vehicles

The use of regenerative braking and recent electrohydraulic or electromechanical brake-by-wire systems with enhanced dynamics enables improved antilock braking control performance in electric vehicles. However, the possible benefit is often limited by suboptimal control architectures with separate distributed controllers for electric motor (EM) and friction brake torque requests. Furthermore, oversimplifications in synthesis models for brake control design constrain the achievable performance. Instead, to date, threshold-based algorithms are widely used in modern production vehicles despite having been designed for slower hydraulic brake systems originally. To exploit the full potential, this work proposes a continuous nonlinear control design using input–output linearization for robust wheel speed tracking control. The design uses a unified controller for the redundant actuators. In addition, it explicitly considers drivetrain oscillations induced by regenerative braking using onboard EMs in the synthesis model. The stability for the resulting zero dynamics is shown through the Lyapunov analysis. The nonlinear controller is augmented by a subsequent control allocation for splitting the control effort on the redundant actuators. The allocation design ensures consistent control performance for both regenerative braking and hybrid braking while simultaneously aiming for high-energy recovery. The overall antilock braking control design is implemented in an electric test vehicle. Its tracking performance, disturbance attenuation, and robustness are experimentally validated through various emergency braking maneuvers on different road surfaces.

A wheel slip control scheme for aeronautical braking applications based on neural network estimation

Due to safety reasons, anti-skid braking control strategies in aircraft can rely only on sensors that are integral to the landing gear, and these currently are limited to wheel rotational speed and braking pressure. Thus, up to now anti-lock-braking systems (ABS) are developed as threshold-based algorithms that regulate the wheel deceleration. However, as it is known from the Automotive field, slip control offers superior closed-loop properties and robustness, paired with a much easier and time-saving tuning phase. This work aims at investigating first of all if slip control does indeed offer the same performance advancement also in the aeronautical braking context and, secondly, whether the wheel slip can be effectively estimated and employed in a closed-loop braking controller without the need of additional sensors on the landing gear. To do this, we propose a wheel slip control scheme where a data-driven approach using a neural network architecture is used to solve the problem of wheel slip estimation. The proposed control scheme is tested within a very realistic simulation setting, capturing all the non-linear effects peculiar of the aeronautical context, and compared with a standard deceleration-based ABS in multiple braking maneuvers carried out within a large operational envelope. The results show that superior performance and robustness can indeed be achieved with the proposed control approach, paving the way for the adoption of wheel slip abs strategies also for aircraft braking.

A data-driven switching control approach for braking systems with constraints

Braking control is of paramount importance in guaranteeing driving safety and comfort, but it is a well-known challenging task, due to the highly nonlinear and road condition-dependent behavior of the vehicle. Existing braking controllers typically rely on accurate models of the vehicle dynamics and the vehicle–road interaction, which are quite difficult to be retrieved in practice. In the wake of the data-driven control paradigm, we propose a model-free and fully data-based braking control method. The architecture of our scheme is two-layered, featuring: an inner switching controller, directly designed from data to match a given closed-loop behavior, and an outer predictive reference governor, exploited to enforce constraints and possibly improve the overall braking performance. The effectiveness of the approach is shown in a simulation environment, by providing a sensitivity analysis to the main tuning knobs of the method.

Optimal Slip Ratio Tracking Integral Sliding Mode Control for an EMB System Based on Convolutional Neural Network Online Road Surface Identification

As the main branch of the brake-by-wire system, the electro-mechanical brake (EMB) system is the future direction of vehicle brake systems. In order to enhance the vehicle braking effect and improve driver safety, a convolutional neural network (CNN) online road surface identification algorithm and an optimal slip ratio tracking integral sliding mode controller (ISMC) combined EMB braking control strategy is proposed in this paper. Firstly, according to the quarter-vehicle model and Burckhardt tire model, the vehicle braking control theory based on the optimal slip ratio is analyzed. Secondly, using the VGG-16 CNN method, an online road surface identification algorithm is proposed. Through a comparative study under the same dataset conditions, it is verified that the VGG-16 method has a higher identification accuracy rate than the SVM method. In order to further improve the generalization ability of VGG-16 CNN image identification, data enhancement is performed on the road surface image data training set, including image flipping, clipping, and adjusting sensitivity. Then, combined with the EMB system model, an exponential approach law method-based ISMC is designed to achieve the optimal slip ratio tracking control of the vehicle braking process. Finally, MATLAB/Simulink software is used to verify the correctness and effectiveness of the proposed strategy and shows that the strategy of real-time identifying road surface conditions through vision can make the optimal slip ratio of vehicle braking control reasonably adjusted, so as to ensure that the adhesion coefficient of wheel braking always reaches the peak value, and finally achieves the effect of rapid braking.

Research on Direct Braking Force Estimation and Control Strategy Using Tire Inverse Model

With the rapid development of computer control and vehicle intelligence technology, speed and safety of vehicles have been greatly improved, and the requirements for vehicle control performance are getting higher and higher. For the direct braking force control, in the process of deceleration, a fast braking response can be obtained, which improves the braking performance and vehicle safety. This paper concentrates on direct braking force estimation and control strategy using a tire inverse model based on the antilock braking system, and to solve the problem of the existing ABS system is mainly antilock braking function, no direct braking force control function. Taking magic formula model for reference inverse model, the critical parameters under different road surfaces are obtained according to experience data. Then, the desired slip ratio corresponding to braking force can be obtained via fast tire inverse model look-up table method. The tyre friction self-adjustment decision making is obtained using the tire inverse model method. A direct braking force antilock braking system (DBF-ABS) controller is built using the nonsingular fast terminal sliding mode method. The simulation results indicated that the control strategy has adaptability and stability to the change of road conditions.

Regenerative Braking Control Strategies with Fixed Ratio and Variable Ratio Braking Forces Optimization Distribution for Electric Vehicles during Downhill Process

Regenerative Braking Control Strategies with Fixed Ratio and Variable Ratio Braking Forces Optimization Distribution for Electric Vehicles during Downhill Process

Regenerative Braking Control Strategy Based on Multi-source Information Fusion under Environment Perception

Regenerative Braking Control Strategy Based on Multi-source Information Fusion under Environment Perception

Research on Control Strategy of Heavy-Haul Train on Long and Steep Downgrades

The control of heavy-haul trains has always been the focus of China’s railway transportation development. One key challenge is the coordination of electric braking and air braking control when the electric-air combined braking is applied on long and steep downgrades. This is normally reliant on manual driving and thus is not cost-effective. To improve the safety and efficiency of train operation in existing heavy-haul railway lines, a multi-label random forest (ML-RF) based approach for heavy-haul train (HHT) operation is proposed. The control characteristics of electric braking and air braking on long and steep downgrades are analyzed first. A prediction model of control strategy is then established with the combination of line conditions and definition of multi-label learning. To evaluate the performance of the model, the 10-fold cross-validation method is adopted. Furthermore, a model parameter optimization algorithm based on evaluation metrics is designed. The feasibility of the proposed approach is demonstrated by the testing results on the actual train running data of one railway line.

Design of an NSMCR Based Controller for All-Electric Aircraft Anti-Skid Braking System

In this paper, a relative threshold event-triggered based novel complementary sliding mode control (NSMCR) algorithm of all-electric aircraft (AEA) anti-skid braking system (ABS) is proposed to guarantee the braking stability and tracking precision of reference wheel slip control. First, a model of the braking system is established in strict-feedback form. Then a virtual controller with a nonlinear control algorithm is proposed to address the problem of constraint control regarding wheel slip rate with asymptotical stability. Next, a novel approaching law-based complementary sliding mode controller is developed to keep track of braking pressure. Moreover, the robust adaptive law is designed to estimate the uncertainties of the braking systems online to alleviate the chattering problem of the braking pressure controller. Additionally, to reduce the network communication and actuator wear of AEA-ABS, a relative threshold event trigger mechanism is proposed to transmit the output of NSMC in demand. The simulation results under various algorithms regarding three types of runway indicate that the proposed algorithms can improve the performance of braking control. In addition, the hardware-in-the-loop (HIL) experimental results prove that the proposed methods are practical for real-time applications.

Retracted: Braking Control System of Oilfield Minor Repair Machine Based on Wireless Sensor Network

Retracted: Braking Control System of Oilfield Minor Repair Machine Based on Wireless Sensor Network

Stochastic model predictive braking control for heavy-duty commercial vehicles during uncertain brake pressure and road profile conditions

When heavy-duty commercial vehicles (HDCVs) must engage in emergency braking, uncertain conditions such as the brake pressure and road profile variations will inevitably affect braking control. To minimize these uncertainties, we propose a combined longitudinal and lateral controller method based on stochastic model predictive control (SMPC) that is achieved via Chebyshev–Cantelli inequality. In our method, SMPC calculates braking control inputs based on a finite time prediction that is achieved by solving stochastic programming elements, including chance constraints. To accomplish this, SMPC explicitly describes the probabilistic uncertainties to be used when designing a robust control strategy. The main contribution of this paper is the proposal of a braking control formulation that is robust against probabilistic friction circle uncertainty effects. More specifically, the use of Chebyshev–Cantelli inequality suppresses road profile influences, which have characteristics that are different from the Gaussian distribution, thereby improving both braking robustness and control performance against statistical disturbances. Additionally, since the Kalman filtering (KF) algorithm is used to obtain the expectation and covariance used for calculating deterministic transformed chance constraints, the SMPC is reformulated as a KF embedded deterministic MPC. Herein, the effectiveness of our proposed method is verified via a MATLAB/Simulink and TruckSim co-simulation.

Automated Braking Decision and Control for Pedestrian Collision Avoidance Based on Risk Assessment

Automated Braking Decision and Control for Pedestrian Collision Avoidance Based on Risk Assessment

Method to Design a Light-Load Regenerative Brake Controller to Suppress the Electrical Vibration of a DC Electrified Railway Vehicle Traction System

Method to Design a Light-Load Regenerative Brake Controller to Suppress the Electrical Vibration of a DC Electrified Railway Vehicle Traction System

Control of BLDC motor and Regenerative Braking in Electric Vehicle

Abstract: With the rising economy, fuel demand also increases. In today's world, fossil fuels are the major contribution to meeting these demands. However, it is not going to be the same scenario in the next decades as the fossil fuels levels are depleting there is a need for an alternative fuel that will satisfy energy requirements and will be a sustainable source in coming years. In the automotive industry petrol and diesel have been major fuels in driving vehicles. Although electric vehicles are going to be replaced by IC engines. Electric vehicles have lower fuel costs as compared to internal combustion engines but because of limited charging stations vehicles are bound to drive within certain distances. With the implementation of regenerative braking in electric vehicles, it is possible to charge batteries when the vehicle is breaking and thus increasing driving distance. This paper presents a simulation of control of BLDC motor with regenerative braking. Keywords: Regenerative Braking, BLDC Motor, Bi-Directional DC-DC Converter, VSI, Electric Drives.

An overview on properties and applications of magnetorheological fluids: Dampers, batteries, valves and brakes

This paper presents a review of literature that introduces the properties and applications of Magnetorheological fluids(MRFs). first, magnetic particles (iron or cobalt), base fluids(oil (mineral-synthetic) or water), and how to prepare magnetorheological fluids are discussed. Then, in the continuation of this research, considering that magnetorheological fluids are smart and soft liquids, the methods of stability and properties (viscosity, hysteresis loop, Shear yield stress, etc) Of these magnetorheological fluids are discussed. Due to the different properties of Magnetorheological fluids, the behavior of these fluids in different states is discussed. These intelligent fluids change their properties when exposed to an external magnetic field. The most important and obvious feature of magnetorheological fluids is their reversibility from liquid to semi-solid state or vice versa in the Presence or the absence of a magnetic field in a fraction of a second. This change in state and properties is known as the magnetorheological effect. This effect depends on various factors, such as the concentration of magnetic particles, the distribution of magnetic particles, the strength of the magnetic field, additives, and so on. The low magnetic effect and instability of magnetorheological fluids are the most important problems against their widespread use in modern industries. According to research, carbonyl iron particles are the most promising particles for the dispersed phase in MRF. The choice of carbonyl iron particles contributes to their high saturation magnetism, relatively low cost, low coercion, and widespread availability. Finally, according to the different properties and behaviors of these fluids, different applications of magnetorheological fluids are discussed. MRF-based control systems are increasingly used in engineering applications such as rheological magnetic electrolytes in batteries, anti-lock braking systems, magnetic clutches, vibrating dampers, shock absorbers, control valves, and various types of vibrating dampers. One of the newest applications of magnetic fluids is the magneto-rheological electrolyte. The use of MRFs in batteries introduces a new class of magnetic field-sensitive electrolytes that has the potential to increase impact resistance, safety, thermal conductivity, and energy storage in electronic devices through reversible active switching electrolyte mechanical properties.

Experimental Confirmation of a Controllable Transmission/Braking Element Consisting of a Functional Elastomer Pasted on a Winding Surface

Electoadhesive (EA) gel can be applied to various mechatronics devices because it provides more degrees of freedom in mechanical design than other functional materials. While case studies have revealed much about these devices, the knowledge attained is still not integrated. We assembled new structured and controllable transmission and braking elements by fixing EA gel on plain and/or winding surfaces. This paper summarizes the characteristics of these devices from experimental findings on the initial pressure on the EA gel surface. The results showed that it is important for the initial pressure to be stable at approximately 0.5–1 kPa.

Adaptive constrained antilock braking control under unknown time-varying slip-friction characteristics

Adaptive constrained antilock braking control under unknown time-varying slip-friction characteristics