In this paper, an improved Model Predictive Control (MPC) controller based on fuzzy adaptive weight control is proposed to solve the problem of autonomous vehicle in the process of path tracking. The controller not only ensures the tracking accuracy, but also considers the vehicle dynamic stability in the process of tracking, i.e., the vehicle dynamics model is used as the controller model. Moreover, the problem of driving comfort caused by the application of classical MPC controller when the vehicle is deviated from the target path is solved. This controller is mainly realized by adaptively improving the weight of the cost function in the classical MPC through the fuzzy adaptive control algorithm. A comparative study which compares the proposed controller with the pure-pursuit controller and the classical MPC controller is made: through the CarSim-Matlab/Simulink co-simulations, the results show that this controller presents better tracking performance than the latter ones considering both tracking accuracy and steering smoothness.

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Path Tracking Control for Autonomous Vehicles Based on an Improved MPC

Automotive driving safety systems such as an anti-lock braking system (ABS) and an electronic stability program (ESP) assist drivers in controlling the vehicle to avoid road accidents. In this paper, ABS and the ESP, based on the fuzzy logic theory, are integrated for vehicle stability control in complex braking maneuvers. The proposed control algorithm is implemented for a sport utility vehicle (SUV) and investigated for braking on different surfaces. The results obtained for the vehicle software simulator confirm the robustness of the developed control strategy for a variety of road profiles and surfaces.

https://www.mdpi.com/2076-3417/6/12/382/pdf

Design and Simulation of the Robust ABS and ESP Fuzzy Logic Controller on the Complex Braking Maneuvers

A multi-layer integrated controller of active front steering and direct yaw moment is developed in this study. In the upper layer, the corrective steering angle and yaw moment are obtained using Sliding Mode Control (SMC) with combined sliding surface. The corrective yaw moments are applied by electric motors embedded in rear wheels. The desired value for yaw rate and sideslip angle are obtained from a Four-Degrees of Freedom (DOF) non-linear vehicle model. In the lower layer, wheel slip and electric motor torque controllers are designed. A 9-DOF non-linear vehicle model is used for simulations and their results illustrate considerable improvements in vehicle handling.

Integrated AFS/DYC sliding mode controller for a hybrid electric vehicle

A yaw stability control system is proposed to improve the handling and stability of a four-independent-wheel drive Electric Vehicle (EV). The control system comprises an Active Front Steering (AFS) controller together with a direct yaw moment controller based on Sliding Mode Control (SMC) and a dynamic force distribution controller based on the optimal Sequential Quadratic Programming Method (SQPM). The function of the control system is to trace the desired yaw rate and meanwhile to keep the sideslip angle of the body Centre of Gravity be minimised. The optimum torque distribution method is applied to adjust the torque of all four wheels. By measuring the vehicle states, the control algorithm determines the level of vehicle stability and intervenes when necessary through individual wheel traction control to provide added stability and handing predictability. The controller system distributes torque and power to each motor to meet the requirements of each wheel. The effectiveness and validation of the proposed control method are evaluated by experiments and simulations. The results manifest that the designed system can assist drivers in controlling the EV's stability during adverse driving manouevres and enhance its performance.

Yaw stability control of a four-independent-wheel drive electric vehicle

A direct yaw-moment control (DYC) method is proposed for the purpose of improving the handling stability of an all in-wheel motor drive electric vehicle (EV). The control system in combination with a fuzzy logic direct yaw-moment controller and an active front steering controller based on sliding mode control. By measurements of vehicle states, the control algorithm determines the level of vehicle stability and intervenes as necessary through individual wheel traction control to provide added stability and handing predictability. Therefore, the stability control system distributes torque and power to each motor to meet the requirements of each wheel. The effectiveness and validation of the proposed control method are evaluated both by Matlab/Simulink based on simulations and experiments. Simulation and experiment results of dynamic responses for sideslip angle and yaw rate of the EV manifest that the DYC system can assist drivers with controlling the stability of the vehicle during adverse driving maneuvers over a variety of road conditions and enhance the handling stability of the EV.

Stability Control System for Four-in-Wheel-Motor Drive Electric Vehicle

In this paper, a fuzzy control method is proposed to improve vehicle yaw stability by integrated yaw moment control and active front steering. This control system is designed by actively controlling the front steering angle and the distribution of braking forces, using feed-forward regulation and feedback revision control strategy, and a fuzzy controller is designed to suppress the output error of yaw rate and side slip angle, which can keep the vehicle to follow the desired trajectories. A simulation based on this control method is performed with a vehicle at two different running conditions, and the simulation results by other different control methods are compared also. The results showed that the presented method can effectively control the yaw rate and side slip angle synchronously, the transient and steady response of vehicle is good, the vehicle yaw stability is improved.

A Fuzzy Control Method to Improve Vehicle Yaw Stability Based on Integrated Yaw Moment Control and Active Front Steering

A yaw stability controller based on generalized predictive control is presented in this paper. The predictive and the desired model of yaw rate are established based on a linearized vehicle model, the future output of yaw rate of the vehicle according to the information of vehicle speed and the steering wheel angle is predicted. Compared with a desired value, a controlling yaw moment is calculated based on the difference between the desired and the actual yaw rate. The predictive control of parameter model applied to the yaw stability control system based on GPC, in the known model situation, can predict the future output of yaw rate of the vehicle precisely and take control action before the yaw instability of vehicle occurs, so this method suits the yaw stability control system of vehicle. The simulation result indicates the yaw rate based on GPC algorithm can track the desired yaw rate well. Compares with the PID control algorithm, the curve of yaw rate has no obvious surge, and the overshoot reduces greatly.

A Research on Generalized Predictive Control for Vehicle Yaw Rate

For air fuel ratio signal of a gasoline engine,there exist transmission delay,which affects the control accuracy of air fuel ratio using directive air fuel ratio sensor signals.A multi-step predictive control method based on neural network was provided in this paper.A multi-step predictive model of air fuel ratio based on back propagation neural network was first set up,and a fuzzy controller was then set up using the error of predictive values and expected values and its derivative.The simulation was accomplished using the experimental data of HL495 gasoline engine,and the results show that using multi-step predictive control method,the air fuel ratio error could be contralled within 3% under the transient conditions.

Air Fuel Ratio Control of Gasoline Engine Based on Neural Network Multi-Step Predictive Model During Transient Conditions

 With the swift development of China’s soft power and the Belt and Road initiative, the cultural interaction between China and the West become more and more frequent. As an art, the film plays a vital role in spreading Chinese culture, and meantime, the film also is a significant means of cross-cultural communication, while the movie subtitle is a carrier to convey the film content and inner spirit to the foreigners. The study analyses the movie subtitle translation of Changjin Lake from three-dimensional selection adaptive conversion perspectives through comparing its source target to its translation target to leave a deeper understanding of translation skills and inner Chinese culture, which will further promote the “going out” of Chinese

From the Perspective of the Three-dimensional Transformations of Eco-translatology to Analyse Movie Subtitle Translation: A Case Study of Changjin Lake

Song Dan-dan

Papers

A Fuzzy Control Method to Improve Vehicle Yaw Stability Based on Integrated Yaw Moment Control and Active Front Steering

A Research on Generalized Predictive Control for Vehicle Yaw Rate

Air Fuel Ratio Control of Gasoline Engine Based on Neural Network Multi-Step Predictive Model During Transient Conditions

From the Perspective of the Three-dimensional Transformations of Eco-translatology to Analyse Movie Subtitle Translation: A Case Study of Changjin Lake