Self Tuning PID Control of Antilock Braking System using Electronic Wedge Brake

This paper describes the design of an antilock braking system (ABS) control for a passenger vehicle that employs an electronic wedge brake (EWB). The system is based on a two-degree-of-freedom (2-DOF) vehicle dynamic traction model, with the EWB acting as the brake actuator. The developed control structure, known as the Self-Tuning PID controller, is made up of a proportional-integral-derivative (PID) controller that serves as the main feedback loop control and a fuzzy supervisory system that serves as a tuner for the PID controller gains. This control structure is generated through two structures, namely FPID and SFPID, where the difference between these two structures is based on the fuzzy input used. An ABS-based PID controller and a fuzzy fractional PID controller developed in previous works were used as the benchmark, as well as the testing method, to evaluate the effectiveness of the controller structure. According to the results of the tests, the performance of the SFPID controller is better than that of other PID and FPID controllers, being 10% and 1% faster in terms of stopping time, 8% and 1% shorter in terms of stopping distance, 9% and 1% faster in terms of settling time, and 40% and 5% more efficient in reaching the target slip, respectively.

Actor-Critic Traction Control Based on Reinforcement Learning with Open-Loop Training

The use of actor-critic algorithms can improve the controllers currently implemented in automotive applications. This method combines reinforcement learning (RL) and neural networks to achieve the possibility of controlling nonlinear systems with real-time capabilities. Actor-critic algorithms were already applied with success in different controllers including autonomous driving, antilock braking system (ABS), and electronic stability control (ESC). However, in the current researches, virtual environments are implemented for the training process instead of using real plants to obtain the datasets. This limitation is given by trial and error methods implemented for the training process, which generates considerable risks in case the controller directly acts on the real plant. In this way, the present research proposes and evaluates an open-loop training process, which permits the data acquisition without the control interaction and an open-loop training of the neural networks. The performance of the trained controllers is evaluated by a design of experiments (DOE) to understand how it is affected by the generated dataset. The results present a successful application of open-loop training architecture. The controller can maintain the slip ratio under adequate levels during maneuvers on different floors, including grounds that are not applied during the training process. The actor neural network is also able to identify the different floors and change the acceleration profile according to the characteristics of each ground.

A control strategy for efficient slip ratio regulation of a pneumatic brake system for commercial vehicles

This paper presents a control strategy for efficient slip ratio regulation of a pneumatic brake system for commercial vehicles. A model-based estimator for brake pressure estimation has been developed. The braking torque applied to the wheel has been computed using the estimated brake pressure for the control of the wheel slip both in braking and traction situations. The vehicle velocity and wheel slip ratio estimation algorithms have been designed using only wheel speed sensors. The proposed slip regulation algorithm has also been successfully implemented for the antilock braking system (ABS) and traction control system (TCS). In ABS, the slip ratio and wheel acceleration are stabilized by a limit cycle control of the braking pressure. The TCS has been implemented by combining engine torque control and pneumatic brake pressure control. The brake controller is based on the valve switched control that incorporates the wheel dynamics and valve on/off characteristics. The ABS and TCS algorithms are integrated into the slip regulation algorithm to reduce the computation load of an Electrical Control Unit (ECU). Four-wheel independent slip monitoring and slip ratio control algorithms have been implemented on the ECU, and their performance has been investigated via both computer simulations and vehicle tests. Both results show that the proposed algorithms enhance the acceleration and braking performance without vehicle acceleration information. Moreover, the proposed split-mu strategy has improved the lateral stability during braking, and the acceleration performance during accelerating on the split-mu road. It has been shown via vehicle tests that, compared to the reference commercial algorithm, the braking distance was reduced by more than 4% on the split-mu and low-mu roads, and the acceleration performance was improved by 7.9% on the split-mu road.

A Modified HOSM Controller Applied to an ABS Laboratory Setup with Adaptive Parameter

The antilock braking system (ABS) is an electromechanical device whose controller is challenging to design because of its nonlinear dynamics and parameter uncertainties. In this paper, an adaptive controller is considered under the assumption that the friction coefficient is unknown. A modified high-order sliding-mode controller is designed to enhance the controller performance. The controller ensures tracking of the desired reference and identifies the unknown parameter, despite parametric variations acting on the real system. The stability proof is done using the Lyapunov approach. Some numerical and experimental tests evaluate the controller on a mechatronic system that represents a quarter-car model.

Research on Intelligent Vehicle Path Tracking with Subsystems Based on Multimodel Intelligent Hierarchical Control Theory

In this paper, a multimodel intelligent hierarchical control (MIHC) algorithm with dual systems is proposed to reduce the performance conflict between a path-tracking motion system and its subsystems during the motion control process of an intelligent vehicle (IV). The working principle of the MIHC algorithm is briefly introduced first, and the dynamic models of IV and the subsystems are constructed. Then, correlation controller models based on MIHC are established. Lastly, the influence of the subsystems on the trajectory tracking of IV is validated through simulations and hardware-in-the-loop test with various condition forms. Results show that the control performance of the automatic steering system has a great influence on the path-tracking accuracy compared with that of the antilock braking system.