Braking Control Research Articles

Robust Adaptive Autonomous Braking Control for Intelligent Electric Vehicles*

This paper presents a two-stage autonomous braking control for electric vehicles with preview information acquired by intelligent transportation systems (ITS) or connected and autonomous vehicle (CAV) technologies. The goal of the control method is firstly to plan a speed profile to comply with a target speed and a target residual distance to an upcoming deceleration event and then to track the planned speed profile by generating tracking-driven braking control torques, which maximally employ regenerative braking. Once deceleration circumstances are perceived, the intelligent reference speed planner computes a smooth speed profile by using a current vehicle speed, a target speed, and a target residual distance. For the braking control to robustly track the planned speed profile, a chattering-free sliding mode controller is designed on the interconnected vehicle and braking system model with braking system uncertainties and external disturbances, and a sufficient condition for the sliding mode gain is introduced. Also, adaptive update laws to compensate for the braking system uncertainties provide feed-forward control efforts for the tracking control inputs. To verify the effectiveness of the proposed control approach, the control methods are applied to a real commercial vehicle and the consecutive driving experiments with various deceleration circumstances illustrate a successful tracking-driven cooperative braking performance.

Time-Delay Deep Q-Network Based Retarder Torque Tracking Control Framework for Heavy-Duty Vehicles

The stability of brake control is an important guarantee for the safety of heavy-duty vehicles (HDVs) at high speeds. However, the electro-hydraulic actuation braking systems often exhibit a significant delay in seconds, which makes braking performance forecasting and control difficult. To address the torque tracking control problem with time delay, a deep inference and control method is proposed. First, a theoretical delay time under different rotating speeds is identified with a data-driven model. Then, a fast end-to-end prediction model is established to estimate the torque performance of the next step with delay information. The deep Q-network (DQN) learning approach is proposed to learn the experimental data by exploring and seeking the optimal control strategy in the time delay environment. A comparative simulation of the proposed DQN-based controller with or without considering time delay, and the rule-based method with or without considering time delay is implemented, and an online processor-in-the-loop (PIL) test with the edge computing device NVIDIA Jetson Xavier NX is performed on the robustness condition. The simulation results and PIL test results demonstrate that the proposed control framework achieves a great improvement in the torque tracking effect with time efficiency.

Model-based and Koopman-based predictive control: a braking control systems comparison

Anti-locking Braking systems are crucial safety systems in modern vehicles. In this work, we investigate the possibility to use Model Predictive Control (MPC) for braking systems by considering three different models identified from data. Specifically, we consider two models, whose structure and the identification procedure are driven by physics principles, and a third black-box modeling approach that relies on Koopman theory. By comparing the effectiveness of the three resulting MPC schemes in a high-fidelity simulation environment, we show that Koopman-based MPC can generally be a viable solution for the design of braking controllers, which might not be the case of nonlinear MPC or approximated scheme like the second one we test.

Robust tuning of Twin-in-the-Loop vehicle dynamics controls via randomized optimization

The Twin-in-the-Loop (TiL) framework for vehicle dynamics control has been recently introduced with the goal of simplifying the end-of-line-tuning phase and enhancing the controller performance. In TiL schemes, high-fidelity vehicle models are run on-board to compute the nominal control action, while a simple closed-loop compensator takes the model-mismatch into account. In this paper, we discuss the robustness properties of the TiL approach to uncertain working conditions. In particular, we show that, due to the model-free nature of the compensator tuning, randomized tools represent an effective way to guarantee a certain level of robustness to different operating conditions with reasonable confidence levels. Simulation results illustrate the effectiveness of the proposed approach within a braking control case study.

Distributed Coordinated Brake Control for Longitudinal Collision Avoidance of Multiple Connected Automated Vehicles

The emerging connected automated vehicle (CAV) technique brings a new opportunity for the development of cooperative collision avoidance systems. This paper proposes a distributed coordinated brake control (DCBC) algorithm for longitudinal collision avoidance of multiple CAVs. Firstly, the studied scenario is defined, within which the communication topology and vehicle dynamics are modeled for a vehicle string and the control objective is designed to minimize the total relative kinetic energy. Next, the control problem is formulated into a centralized model predictive control problem, which is then transformed into a quadratic programming (QP) problem. By separating the QP problem into several sub-problems assigned to each CAV, distributed optimization is introduced to solve the QP problem in a distributed manner. Elaborate numerical simulation comparisons are conducted to demonstrate the advantage of the proposed DCBC algorithm.

Active and Data-driven Health and Usage Monitoring of Aircraft Brakes

Aircraft brakes are a safety-critical subsystem, and their prolonged use in each landing maneuver makes them subject to significant wear. Thus, it is crucial to devise efficient methods for monitoring their correct functioning and their health and usage status using the signals available in the Brake Control Unit. This paper proposes and validates an innovative data-driven approach to this problem. The proposed architecture is integrated with the Anti-lock Braking System algorithm providing combined health monitoring and anomaly detection for aircraft brakes in addition to an online estimate of the residual useful life of these components.

Research on Composite Braking Control Strategy of Four-Wheel-Drive Electric Vehicles With Multiple Motors Based on Braking Energy Recovery Optimization

Research on Composite Braking Control Strategy of Four-Wheel-Drive Electric Vehicles With Multiple Motors Based on Braking Energy Recovery Optimization

Development of Driving Robot Applied Regenerative Brake Control of Electrified Vehicles

Development of Driving Robot Applied Regenerative Brake Control of Electrified Vehicles

Retracted: Research on Compound Braking Control Strategy of Extended-Range Electric Vehicle Based on Driving Intention Recognition.

[This retracts the article DOI: 10.1155/2022/8382873.].

Fast yet predictable braking manoeuvers for real-time robot control

This paper proposes a framework for generating fast, smooth and predictable braking manoeuvers for a controlled robot. The proposed framework integrates two approaches to obtain feasible modal limits for designing braking trajectories. The first approach is realtime capable but conservative considering the usage of the available feasible actuator control region, resulting in longer braking times. In contrast, the second approach maximizes the used braking control inputs at the cost of requiring more time to evaluate larger, feasible modal limits via optimization. Both approaches allow for predicting the robot's stopping trajectory online. In addition, we also formulated and solved a constrained, nonlinear final-time minimization problem to find optimal torque inputs. The optimal solutions were used as a benchmark to evaluate the performance of the proposed predictable braking framework. A comparative study was compiled in simulation versus a classical optimal controller on a 7-DoF robot arm with only three moving joints. The results verified the effectiveness of our proposed framework and its integrated approaches in achieving fast robot braking manoeuvers with accurate online predictions of the stopping trajectories and distances under various braking settings.

Regenerative braking control strategy optimization based on hybrid power source and PSO

Regenerative braking control strategy optimization based on hybrid power source and PSO

Intelligent Brake Controller Based on Intelligent Highway Signs to Avoid Accidents on Algerian Roads

Intelligent Brake Controller Based on Intelligent Highway Signs to Avoid Accidents on Algerian Roads

Research on Design Optimization and Simulation of Regenerative Braking Control Strategy for Pure Electric Vehicle Based on EMB Systems

Research on Design Optimization and Simulation of Regenerative Braking Control Strategy for Pure Electric Vehicle Based on EMB Systems

A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car

The automatic emergency braking (AEB) system of the passenger car is responsible for auxiliary braking judgment and decision-making in an emergency. Due to the inevitable pressure response delay of passenger car pneumatic braking systems, a large number of verification tests should be carried out to propose appropriate strategies and algorithms. To realize the rapid verification of the AEB control algorithm, a verification system integrating software-in-the-loop (SIL) and hardware-in-the-loop (HIL) was proposed for a two-axle passenger car. It can verify the logic feasibility of the control algorithm through SIL testing, and can verify the implementation effect of the control algorithm through HIL testing. The verification system is composed of IPG, dSPACE, and a pneumatic braking bench. Considering the influence of pneumatic braking delay, it is well-matched with the actual vehicle AEB system. The AEB hierarchical control algorithm was verified under three typical test conditions. The results show that the SIL testing results of speed and relative distance are in good agreement with the HIL testing results, and the average relative deviation of relative distance is only 1.7 m. The single test time of the SIL testing is about 228 s, which can meet the requirements of rapid verification of the AEB control algorithm of the passenger car.

Development of robust braking controller for autonomous vehicles to achieve any ride comfort performance

Development of robust braking controller for autonomous vehicles to achieve any ride comfort performance

Алгоритм управления рекуперативным торможением с помощью педали акселератора

Modern manufacturers of electric vehicles everywhere introduce regenerative braking control using the accelerator pedal. One of the difficulties in implementing regenerative braking is the interaction of the service braking system and the auxiliary one using a traction motor. In this regard, the concept of regenerative braking control with one pedal – the accelerator pedal. Currently, a similar concept is presented on many modern electric cars, such as the Tesla Model S, Tesla Model X, Hyundai Kona. The aim of study is the creation and subsequent debugging of an algorithm for controlling regenerative braking using the accelerator pedal in relation to urban public electric transport. The developed algorithm is implemented in the MATLAB/Simulink simulation environment. Using the available blocks and subsystems, a mathematical model of the movement of an electric vehicle has been compiled. A mathematical model of the movement of electric vehicle has been developed that allows testing and debugging of the algorithms being developed. Simulation methods have confirmed the operability of the proposed method of regenerative braking control by the accelerator pedal. The scientific novelty of the research lies in the synthesis of an algorithm for controlling regenerative braking in relation to the operation of public electric transport in an urban environment with frequent stops, traffic on roads with slopes and starting to climb. The practical utility (value) of this research lies in the possibility of using the proposed control algorithm in urban public electric transport with on-board energy storage, for example, in electric buses.

Optimization of the Control of Electromagnetic Brakes in the Stand for Tuning Internal Combustion Engines Using ID Regulators of Fractional Order

This work is aimed at developing a stand for tuning the fuel system of an internal combustion engine based on two electromagnetic retarders connected to the driving wheels of a car to simulate a load, and a microprocessor-based torque control system for each brake. In accordance with the terms of reference from the specialists of the automotive service center, such a stand should provide two main modes of operation: (1) stabilization of the speed of the drive wheels in the entire range of loads (fuel supply); (2) engine acceleration and deceleration according to linear tachograms in the range from minimum to maximum speed to determine the dependence of engine power and torque on speed. The purpose of this research is the synthesis of controllers, testing, the choice of the structural scheme, and the parameters of the control and data processing system in the stand for the precision tuning of internal combustion engines. Based on a preliminary analysis of the system, taking into account the mechanical connection of the wheels through the main gear and the car differential, the nonlinear dependence of the electromagnetic torque on the current and retarder speed, and subsequent experimental results, we obtained two types of controller—a third-order aperiodic transfer function and a fractional aperiodic transfer function of order 1.6. This made it possible to synthesize a family of controllers that ensure the operation of the stand in the required modes: synchronization of wheel speeds during engine acceleration; stabilization of the reference speed when the engine torque is changed from minimum to maximum due to fuel supply; measurement of the maximum power and torque of the internal combustion engine during the formation of a triangular tachogram with a given acceleration to compensate for the dynamic component of the torque due to mechanical inertia. The system with the PID controller configured in MATLAB in the “Tune” package has the best performance, but the smallest overshoot and the best dynamic accuracy are ensured by the PIDIγIµ fractional–integral controller, where the system is characterized by a fractional order of astaticism 1.6. Such a controller for each electromagnetic retarder serves as the basis of the microprocessor-based control, data acquisition, processing, graphical display system, and will also be used to develop a similar bench for tuning four-wheel-drive vehicles.

A Study of a Cornering Braking Control System for a Motorcycle

Motorcycles are widely used in people’s daily lives for their convenience. Due to their characteristic of static instability, they have a larger roll angle than cars when turning, which brings the hidden danger of overturning. When a motorcycle is turning and braking simultaneously, the overturn risk rises dramatically. This study presents a novel control system that can ensure stability and braking performance during turning and braking. Given the direct impact of tires on a motorcycle’s behavior, a motorcycle tire model was created via Magic Formula to determine the kinematic parameters that have correlated effects on the longitudinal and lateral characteristics of tires. The slip ratio was defined as the manipulated variable, and a constrained optimization model that aimed to maximize the braking performance and took the stability as the constraint condition was created and solved through the gold section method. The obtained optimal slip ratio was then used as the input for the proposed cornering braking control system that adopted the Fuzzy PID algorithm. Finally, the feasibility of the proposed controller was tested via a co-simulation method, and the simulation results were compared with an ordinary anti-lock braking system. The results demonstrate that the proposed cornering braking control system can take both motorcycle stability and braking performance into consideration at the same time, effectively increasing the security of a motorcycle during braking in a turn.

Current Non-Contact Road Surface Condition Detection Schemes and Technical Challenges

Given the continuous improvement in the capabilities of road vehicles to detect obstacles, the road friction coefficient is closely related to vehicular braking control, thus the detection of road surface conditions (RSC), and the level is crucial for driving safety. Non-contact technology for RSC sensing is becoming the main technological and research hotspot for RSC detection because of its fast, non-destructive, efficient, and portable characteristics and attributes. This study started with mapping the relationship between friction coefficients and RSC based on the requirement for autonomous driving. We then compared and analysed the main methods and research application status of non-contact detection schemes. In particular, the use of infrared spectroscopy is expected to be the most approachable technology path to practicality in the field of autonomous driving RSC detection owing to its high accuracy and environmental adaptability properties. We systematically analysed the technical challenges in the practical application of infrared spectroscopy road surface detection, studied the causes, and discussed feasible solutions. Finally, the application prospects and development trends of RSC detection in the fields of automatic driving and exploration robotics are presented and discussed.

Design of differential braking control system for Trailer-Mounted Recreational Vehicle

Design of differential braking control system for Trailer-Mounted Recreational Vehicle