Dynamic Lane Tracking Control of the Commercial Vehicle Based on RMPC Algorithm Considering the State of Preceding Vehicle

Tang, Bin; Hu, Zitian; Jiang, Haobin; Yin, Yue; Yang, Zhengyi

doi:10.3390/machines10070534

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…With the rapid development of electronic control technology, advanced electronic control systems such as adaptive cruise control, vehicle stability control, direct yaw torque control, and anti-lock braking control have been widely used in vehicles [1][2][3][4]. This not only greatly improves the driving stability and safety of the vehicle but also makes the vehicle have a certain autonomous performance.…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation of Driving State and Road Surface Adhesion Coefficient of a Four-Wheel Independent and Steering-Drive Electric Vehicle

Chen,

Li,

Zhang

et al. 2024

WEVJ

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Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal Kalman filtering and an intelligent bionic antlion optimization algorithm. Firstly, according to the research purpose of the paper and the focus on the accuracy of the establishment of the three degrees of freedom dynamics model, fully considering the road conditions, the paper adopts the Dugoff tire model and finally completes the establishment of the vehicle state estimation model. Secondly, the drive state estimation algorithm is developed utilizing the principles of federal Kalman filtering and volume Kalman filtering. At the same time, robust estimation theory is introduced into the sub-filter, and the antlion optimization module is designed at the lower layer of the main filter to enhance the accuracy of estimates. It is easy to see that the design of the Antlion federal Kalman travel state estimation algorithm has noticeably enhanced accuracy and traceability, according to the result. Thirdly, a joint estimation algorithm of state estimation and road surface adhesion coefficient has been devised to enhance the stability and precision of the estimation process. Finally, the results showed that the joint estimation algorithm has high accuracy in estimating vehicle driving state parameters such as the center of mass lateral deflection angle and road surface adhesion coefficient by simulation.

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Section: Introductionmentioning

confidence: 99%

Joint Estimation of Driving State and Road Surface Adhesion Coefficient of a Four-Wheel Independent and Steering-Drive Electric Vehicle

Chen,

Li,

Zhang

et al. 2024

WEVJ

View full text Add to dashboard Cite

show abstract

“…The latter can be combined with anti-collision warning control systems that warn the driver if such an obstacle is detected within the vehicle direction. Clearly, the above applications have received significant attention from researchers [4,5]. Still, however, the suspension control system, which is responsible for controlling and adjusting the ride comfort with an aim of reducing oscillations, is always at the forefront of research and lectures in engineering schools (mainly in undergraduate studies).…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of a Feedback Controller for an Active Suspension System: A Simplified Approach

Provatas,

Ipsakis

2023

Processes

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The concept of controlling vehicle comfort is a common problem that is faced in most under- and postgraduate courses in Engineering Schools. The aim of this study is to provide a simplified approach for the feedback control design and simulation of active suspension systems, which are applied in vehicles. Firstly, the mathematical model of an active suspension system (a quarter model of a car) which consists of a passive spring, a passive damper and an actuator is provided. In this study, we chose to design and compare the following controllers: (a) conventional P, PI and PID controllers that were tuned through two conventional methodologies (Ziegler–Nichols and Tyreus–Luyben); (b) an optimal PID controller that was tuned with a genetic algorithm (GA) optimization framework in terms of the minimization of certain performance criteria and (c) an internal model controller (IMC) based on the process transfer function. The controllers’ performance was assessed in a series of realistic scenarios that included set-point tracking with and without disturbances. In all cases, the IMC controller and the optimal PID showed superior performance. On the other hand, the P and PI controllers showed a rather insufficient behavior that involved persistent errors, overshoots and eventually, uncomfortable ride oscillations. Clearly, a step-by-step approach such as this, that includes modeling, control design and simulation scenarios can be applied to numerous other engineering examples, which we envisage to lead more students into the area of automatic control.

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Dynamic Lane Tracking Control of the Commercial Vehicle Based on RMPC Algorithm Considering the State of Preceding Vehicle

Cited by 2 publications

References 32 publications

Joint Estimation of Driving State and Road Surface Adhesion Coefficient of a Four-Wheel Independent and Steering-Drive Electric Vehicle

Joint Estimation of Driving State and Road Surface Adhesion Coefficient of a Four-Wheel Independent and Steering-Drive Electric Vehicle

Design and Simulation of a Feedback Controller for an Active Suspension System: A Simplified Approach

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