Heading tracking of 6WID/4WIS unmanned ground vehicles with variable wheelbase based on model free adaptive control

Jiang, Yue; Xu, Xiaojun; Zhang, Lei

doi:10.1016/j.ymssp.2021.107715

Cited by 38 publications

(15 citation statements)

References 38 publications

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“…The serial unmanned vehicle system THMR is equipped with color cameras, laser distance finder, magnetic compass, optical code disk, and other positioning system, and the system enjoys a high level of action decision-making and planning ability [80]. HQ3 unmanned car of Red flag from Defense University of Science and Technology [81] and series intelligent cars from Jilin University, JLUIV, and DLIUV, as well as the intelligent vehicles from Shanghai Jiaotong University, Chang'an University, Hunan University, and Hefei Institute of Material Science, Chinese Academy of Sciences, also adopted a variety of environmental sensing equipment and multisource information fusion technology, realizing their own positioning, navigation, obstacle avoidance, and following functions [82]. Document [83] proposes Multi-View 3D Object Protection Network (MV3D) for a groundbreaking fusion of LiDAR point cloud data with RGB image information.…”

Section: Multisource Sensor Information Fusionmentioning

confidence: 99%

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

et al. 2021

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Environmental perception technology is the basis and premise of intelligent vehicle decision control of intelligent vehicles, a crucial link of intelligent vehicles to realize intelligence, and also the basic guarantee of its safety and intelligence. The accuracy and robustness of the perception algorithm will directly affect or even determine the realization of the upper function of intelligent vehicles. The wrong environmental perception will affect the control of the vehicle, thus causing safety risks. This paper discusses the intelligent vehicle perception technology and introduces the development status and control strategies of several important sensors such as machine vision, laser radar, and millimeter-wave radar. Target detection, target recognition, and multisensor fusion are analyzed in the optimized part of sensor results. The functions of the intelligent vehicle assistance system which has been applied to the ground at present are described, and the lane detection, adaptive cruise control (ACC), and autonomous emergency braking (AEB) are analyzed. Finally, the paper looks forward to the research direction of sense-based intelligent vehicle perception technology, which will play an important role in guiding the development of intelligent vehicles and accelerate the landing process of intelligent vehicles.

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Section: Multisource Sensor Information Fusionmentioning

confidence: 99%

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

et al. 2021

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“…In addition, the method also has characteristics of strong adaptability and robustness. 10 The method was widely used in many fields, such as robot bionic device, 11 aircraft control, 12 wind turbine control, 13 vehicle lateral, and longitudinal control, 14,15 etc. But its disadvantage is that the control effect is bad for the hysteretic system.…”

Section: Introductionmentioning

confidence: 99%

Model-free adaptive sliding mode control for intelligent vehicle longitudinal dynamics

Zhang-qi

Jiang

Wei

et al. 2022

Advances in Mechanical Engineering

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Vehicle longitudinal dynamics system has the characteristics of being strongly non-linear, time-varying, and multiple-perturbed, so, it is difficult to build the mathematical model accurately. The control algorithms, based on accurate mathematical model, can hardly achieve the ideal effect, but control methods, which merely adopt input/output data (I/O) of a system, provides a solution. In this paper, by means of combing model-free adaptive control (MFAC) and sliding-mode control (SMC), the model-free adaptive sliding mode control (MFASMC) method is proposed. By comparison with feedback-feedforward control method, the MFASMC method can better improve the control effect and anti-disturbance performance. Meanwhile, the stability of MFASMC method was proven mathematically. Besides, the parameters of MFASMC method were optimized using genetic algorithm. Results of simulation and HiL test shows that the MFASMC method has fast response, strong robustness and smooth output. It would be better to apply it to the longitudinal dynamics control of intelligent vehicles.

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“…On the other hand, in [14], an algorithm is proposed that joins model free adaptive control (MFAC) and particle swarm optimization (PSO) techniques to improve the position tracking of unmanned ground vehicles. For this, they have a GPS, a sensor to measure the angle of rotation of the wheel (which are fused by the mathematical model of the car) and an INS.…”

Section: Introductionmentioning

confidence: 99%

GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

et al. 2021

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GPS sensors are widely used to know a vehicle’s location and to track its route. Although GPS sensor technology is advancing, they present systematic failures depending on the environmental conditions to which they are subjected. To tackle this problem, we propose an intelligent system based on fuzzy logic, which takes the information from the sensors and correct the vehicle’s absolute position according to its latitude and longitude. This correction is performed by two fuzzy systems, one to correct the latitude and the other to correct the longitude, which are trained using the MATLAB ANFIS tool. The positioning correction system is trained and tested with two different datasets. One of them collected with a Pmod GPS sensor and the other a public dataset, which was taken from routes in Brazil. To compare our proposal, an unscented Kalman filter (UKF) was implemented. The main finding is that the proposed fuzzy systems achieve a performance of 69.2% higher than the UKF. Furthermore, fuzzy systems are suitable to implement in an embedded system such as the Raspberry Pi 4. Another finding is that the logical operations facilitate the creation of non-linear functions because of the ‘if else’ structure. Finally, the existence justification of each fuzzy system section is easy to understand.

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Heading tracking of 6WID/4WIS unmanned ground vehicles with variable wheelbase based on model free adaptive control

Cited by 38 publications

References 38 publications

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

Model-free adaptive sliding mode control for intelligent vehicle longitudinal dynamics

GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

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