A Trajectory Tracking Control Strategy of 4WIS/4WID Electric Vehicle with Adaptation of Driving Conditions

Zheng, Hongyu; Yang, Shuo

doi:10.3390/app9010168

Cited by 30 publications

(16 citation statements)

References 32 publications

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“…In Chaib et al (2004), Chaib controlled the lateral distance of the vehicle through the combination of PID and H∞ control methods, and it has better robustness while implementing tracking control. In Hongyu Zheng and Yang (2018), the multiple optimization objectives, including vehicle stability performance and tire wear energy consumption objectives, are determined, and the weight coefficient is adaptive to different working conditions based on the fuzzy logic theory. In Hima et al (2011), Hima constructed a two-dimensional fuzzy decision maker with the lateral distance error and heading angle error, and he carried out fuzzy PID control on the front wheel to accomplish the path tracking of the vehicle.…”

Section: R E T R a Cmentioning

confidence: 99%

RETRACTED: A new vehicle’s steering model and trajectory tracking based on online motion state estimation and adaptive fuzzy control method

Liu

Chen

Sun

et al. 2020

Journal of Vibration and Control

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The roadside stone paver plays an important role in improving the road construction quality and speeding up the road construction progress. Faced with the high requirement for lateral distance precision, this study proposes a motion state estimation and fuzzy control method for trajectory tracking of the unmanned cement paver. The roadside stone paver has three crawler wheels, which are steered by cylinders. Based on the vehicle’s characteristics, the kinematic and dynamic models of the vehicle were established. For trajectory tracking, the values of front and back steering sensors were filtered to calculate the current lateral distance. Through the combination of the state prediction model and cylinder length constraints, the cylinders’ target lengths were calculated to obtain satisfactory results for the next time. Then, an adaptive fuzzy controller was designed to track the cylinders’ target lengths. It could reduce the calculation time effectively and maintain linear characteristics for the valve-controlled nonlinear system. To verify the validity of the motion state estimation and fuzzy control algorithm, a cosimulation was conducted on Simulink and Amesim software. Moreover, based on a new roadside stone paver, different algorithms were used for effect comparison. The simulation and experimental results show that the motion state estimation and fuzzy control algorithm can keep vehicle stability and reduce the error of trajectory tracking.

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Section: R E T R a Cmentioning

confidence: 99%

RETRACTED: A new vehicle’s steering model and trajectory tracking based on online motion state estimation and adaptive fuzzy control method

Liu

Chen

Sun

et al. 2020

Journal of Vibration and Control

View full text Add to dashboard Cite

show abstract

“…The centralized control structure is based on the characteristics of the vehicle's dynamics, and the subsystems are controlled directly by a centralized controller. The controller's design is generally based on linear or nonlinear models, and the method for designing a multi-output multi-input system (MIMO) is adopted to solve problems related to longitudinal and lateral vehicle dynamics [44]. The steering system of the autonomous shuttle buses is powered by an electric gear motor that drives the steering gear (1), which turns and activates the rack (2).…”

Section: High-voltage Batterymentioning

confidence: 99%

Autonomous Shuttle Bus for Public Transportation: A Review

2020

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The rapid evolution of autonomous technology in the field of automotive and information technology (IT) has made it possible to implement autonomous vehicles (AVs) for public passenger transport. Although the shuttle bus transport capacities currently in use are low (maximum 15 people), the use of these transport units in large urban agglomerations is beneficial for society. The current paper is written to review the current AV implementation with respect to shuttle buses with its direct implications in their scientific evolution, with direct links to the legal and social aspects of public transportation all over the world. A critical aspect that is presented in the paper is the legal framework of autonomous driving, which is extremely uneven around the globe, with the direct impact of autonomous shuttle bus exploitation. As the legislation on AVs presents some shortcomings in the approval, registration, and public road implementation of these vehicles, many of the world’s major cities have found ways to integrate them into testing programs, establishing the basis for future comprehensive legislative measures in this highly dynamic scientific domain. The current technological solutions adopted by several autonomous shuttle bus producers will be presented with an exhaustive overview of each major component. The aspects of the control algorithm, with its complicated layers of security and perturbance factors, will be explained in detail. Thus, in some countries/cities, autonomous shuttle buses have been implemented on less-traveled routes where they can travel at speeds up to 25 km/h without hindering the public’s circulation, such as university campuses, industrial areas, airports, and sports bases. Some countries/cities use autonomous shuttle buses for pilot programs related to passenger transport, while others use them in postal transport and others for scientific purposes. In all of these situations, the first step in autonomous driving has been taken. The paper also makes an evaluation of the social factors that are a consequence of the mass introduction of autonomous driving as a means of public transportation. Autonomous shuttle buses are becoming a part of everyday life in big cities. Their acceptance as a strategic means of transport depends on their efficiency in daily services; through its efficiency, this means of transport will become a game-changer once its benefits become not only known but experienced by a large number of users.

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“…where R 2 is a constant matrix. Equation (32) must satisfy the degenerate Riccati equation, and the control vector can be obtained by submitting K L into equation (25).…”

Section: Lqr Tracking Stability Controlmentioning

confidence: 99%

“…However, these methods neglect the environment constraints and system capabilities. In order to make full use of road friction coefficient, stability is controlled in the tire force distribution layer with the hierarchical control method for trajectory tracking [24][25][26]. In addition, a variety of studies consider more constraints to enhance stability [27,28].…”

Section: Introductionmentioning

confidence: 99%

Coordinated Stability Control Strategy for Intelligent Electric Vehicles Using Vague Set Theory

Zhao

et al. 2020

Mathematical Problems in Engineering

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Aiming at improving the tracking stability performance for intelligent electric vehicles, a novel stability coordinated control strategy based on preview characteristics is proposed in this paper. Firstly, the traditional stability control target is introduced with the two degrees of freedom model, which is realized by the sliding mode control strategy. Secondly, an auxiliary control target further amending the former one with the innovation formulation of the preview characteristics is established. At last, a multiple purpose Vague set leverages the contribution of the traditional target and the auxiliary preview target in various vehicle states. The proposed coordinated control strategy is analyzed on the MATLAB/CarSim simulation platform and verified on an intelligent electric vehicle established with A&D5435 rapid prototyping experiment platform. Simulation and experimental results indicate that the proposed control strategy based on preview characteristics can effectively improve the tracking stability performance of intelligent electric vehicles. In the double lane change simulation, the peak value of sideslip angle, yaw rate, and lateral acceleration of the vehicle is reduced by 13.2%, 11.4%, and 8.9% compared with traditional control strategy. The average deviations between the experimental and simulation results of yaw rate, lateral acceleration, and steering wheel angle are less than 10% at different speeds, which demonstrates the consistency between the experimental and the simulation results.

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A Trajectory Tracking Control Strategy of 4WIS/4WID Electric Vehicle with Adaptation of Driving Conditions

Cited by 30 publications

References 32 publications

RETRACTED: A new vehicle’s steering model and trajectory tracking based on online motion state estimation and adaptive fuzzy control method

RETRACTED: A new vehicle’s steering model and trajectory tracking based on online motion state estimation and adaptive fuzzy control method

Autonomous Shuttle Bus for Public Transportation: A Review

Coordinated Stability Control Strategy for Intelligent Electric Vehicles Using Vague Set Theory

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