Distributed formation control of nonholonomic autonomous vehicle via RBF neural network

Yang, Shichun; Cao, Yaoguang; Peng, Zhaoxia; Wen, Guoguang; Guo, Konghui

doi:10.1016/j.ymssp.2016.04.015

Cited by 70 publications

(43 citation statements)

References 26 publications

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“…RBF neural network consensus-based distributed control scheme is proposed for non-holonomic autonomous vehicles in a pre-defined formation along the specified reference trajectory [40]. Luo proposed a deep convolution neural network that is no less than nine layers.…”

Section: Fault Diagnosis Of a Vehiclementioning

confidence: 99%

An Integrated Self-Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning

et al. 2018

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This paper proposes "An Integrated Self-diagnosis System (ISS) for an Autonomous Vehicle based on an Internet of Things (IoT) Gateway and Deep Learning" that collects information from the sensors of an autonomous vehicle, diagnoses itself, and the influence between its parts by using Deep Learning and informs the driver of the result. The ISS consists of three modules. The first In-Vehicle Gateway Module (In-VGM) collects the data from the in-vehicle sensors, consisting of media data like a black box, driving radar, and the control messages of the vehicle, and transfers each of the data collected through each Controller Area Network (CAN), FlexRay, and Media Oriented Systems Transport (MOST) protocols to the on-board diagnostics (OBD) or the actuators. The data collected from the in-vehicle sensors is transferred to the CAN or FlexRay protocol and the media data collected while driving is transferred to the MOST protocol. Various types of messages transferred are transformed into a destination protocol message type. The second Optimized Deep Learning Module (ODLM) creates the Training Dataset on the basis of the data collected from the in-vehicle sensors and reasons the risk of the vehicle parts and consumables and the risk of the other parts influenced by a defective part. It diagnoses the vehicle's total condition risk. The third Data Processing Module (DPM) is based on Edge Computing and has an Edge Computing based Self-diagnosis Service (ECSS) to improve the self-diagnosis speed and reduce the system overhead, while a V2X based Accident Notification Service (VANS) informs the adjacent vehicles and infrastructures of the self-diagnosis result analyzed by the OBD. This paper improves upon the simultaneous message transmission efficiency through the In-VGM by 15.25% and diminishes the learning error rate of a Neural Network algorithm through the ODLM by about 5.5%. Therefore, in addition, by transferring the self-diagnosis information and by managing the time to replace the car parts of an autonomous driving vehicle safely, this reduces loss of life and overall cost.

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Section: Fault Diagnosis Of a Vehiclementioning

confidence: 99%

An Integrated Self-Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning

et al. 2018

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“…where v c and w c are the command values of v and w, respectively, k x and k are positive constants,̇x anḋare the first-order time derivatives of x and , respectively, which, due to (11) and the assumptionṡs…”

Section: Kinematic Controllermentioning

confidence: 99%

“…The motion control of wheeled mobile robots (WMRs) with nonholonomic constraints has been a hot topic in control area for several decades due to its wide application (see other works [1][2][3][4][5][6][7][8][9][10][11] and the references therein) and due to the fact that, under Brockett's necessary condition, 12 nonholonomic systems cannot be stabilized by time-invariant state feedback controls. To circumvent this difficulty, various control schemes were proposed for the stabilization of WMRs or similar nonholonomic systems.…”

Section: Introductionmentioning

confidence: 99%

Robust neural network–based tracking control and stabilization of a wheeled mobile robot with input saturation

Huang

Zhou

et al. 2018

Intl J Robust & Nonlinear

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This paper presents a robust neural network-based control scheme to deal with the problem of tracking and stabilization simultaneously for a wheeled mobile robot subject to parametric uncertainties, external disturbances, and input saturation. At first, a new error-state transformation scheme is designed by introducing some auxiliary variables as an additional virtual control signals to reduce the adverse effect caused by the underactuation. These variables can change their structures for different desired trajectories to be tracked. Then, a robust control law is proposed combining with a kinematic controller and a dynamic controller, while a three-layer neural network system is applied to approximate model uncertainties. Stability analysis via the Lyapunov theory shows that the proposed controller can make tracking errors converge to bounded neighborhoods of the origin. Finally, some simulation results are illustrated to verify the effectiveness of the proposed control strategy.

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“…Considering the advantages of RBF neural networks that are easier to design, and have faster training speed, higher training accuracy, stronger generalization ability, and stronger tolerance for input noise [26], this paper selects a RBF neural network as a competitive method for predicting the viscosity characteristics of different ethylene glycol/water based nanofluids with different influence factors. Firstly, the basis theory and modeling process of the RBF neural network are introduced briefly.…”

Section: Introductionmentioning

confidence: 99%

Viscosity Prediction of Different Ethylene Glycol/Water Based Nanofluids Using a RBF Neural Network

Zhao

2017

Applied Sciences

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Abstract:In this study, a radial basis function (RBF) neural network with three-layer feed forward architecture was developed to effectively predict the viscosity ratio of different ethylene glycol/water based nanofluids. A total of 216 experimental data involving CuO, TiO 2 , SiO 2 , and SiC nanoparticles were collected from the published literature to train and test the RBF neural network. The parameters including temperature, nanoparticle properties (size, volume fraction, and density), and viscosity of the base fluid were selected as the input variables of the RBF neural network. The investigations demonstrated that the viscosity ratio predicted by the RBF neural network agreed well with the experimental data. The root mean squared error (RMSE), mean absolute percentage error (MAPE), sum of squared error (SSE), and statistical coefficient of multiple determination (R 2 ) were respectively 0.04615, 2.12738%, 0.46007, and 0.99925 for the total samples when the Spread was 0.3. In addition, the RBF neural network had a better ability for predicting the viscosity ratio of nanofluids than the typical Batchelor model and Chen model, and the prediction performance of RBF neural networks were affected by the size of the data set.

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Distributed formation control of nonholonomic autonomous vehicle via RBF neural network

Cited by 70 publications

References 26 publications

An Integrated Self-Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning

An Integrated Self-Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning

Robust neural network–based tracking control and stabilization of a wheeled mobile robot with input saturation

Viscosity Prediction of Different Ethylene Glycol/Water Based Nanofluids Using a RBF Neural Network

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