Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

2022 ◽  
pp. 095440702110724
Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Commercial Vehicles ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Wide Range ◽  
The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

Vehicle Sideslip Angle Estimation Through Neural Networks: Application to Experimental Data

2006 ◽  
Author(s):  
Stefano Melzi ◽  
Edoardo Sabbioni ◽  
Alessandro Concas ◽  
Marco Pesce
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Lateral Acceleration ◽  
Slip Angle ◽  
Vehicle Speed ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Training Stage ◽  
Wide Range ◽  
Self Adaptation

This work explores the possibility of using a non-structured algorithm as a sideslip angle valuer: on the basis of a preliminary numerical analysis, a neural network was designed and trained with experimental signals of lateral acceleration, vehicle speed, yaw rate and steer angle. The network was applied to experimental data in order to verify its capability of self-adaptation to changes in friction coefficient and to provide accurate estimations for manoeuvres sensibly different from the ones used during the training stage. The simple architecture joined with an appropriate training set conferred good self-adaptation properties to the neural network which was able to provide satisfying estimation of side slip angle for a wide range of manoeuvres and different friction conditions.

scholarly journals METHOD OF CONSTRUCTION OF A NEUROREGULATOR MODEL WHEN OPTIMIZING THE CONTROL STRUCTURE OF A TECHNOLOGICAL CYCLE

Doklady BGUIR ◽  
2019 ◽  
pp. 125-132
Author(s):  
V. S. Smorodin ◽  
V. A. Prokhorenko
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Phase Plane ◽  
Control Structure ◽  
Movement Direction ◽  
Technological Cycle ◽  
The Neural Network ◽  
System States ◽  
The Mathematical Model ◽  
The Given

In this paper authors present the results of a research that had a purpose to develop a method of constructing a neuroregulator model for the case of optimization of the control structure of a technological cycle. The method's implementation is based upon the automation of a production process when a physical controller, that operates the technological process according to a given program, is present. In order to achieve this goal, the artificial neural network approaches were implemented to create a mathematical model of the neuroregulator. The mathematical model of the neuroregulator is based on a physical prototype, and the procedure of a real-time control synthesis (adaptive control) is based on recurrent neural network training. The neural network architecture includes LSTM blocks, which are capable of storing information for long periods of time. A method is proposed for constructing a neuroregulator model for control of a production cycle when solving the task of the optimal trajectory finding on the phase plane of the technological cycle states. In the considered task of the optimal trajectory finding the mathematical model of the neuroregulator receives at each moment of time information about the current system state, the adjacent system states and the movement direction on the phase plane of states. Movement direction is determined by the given control optimization criteria. Based on the research results it was found that recurrent networks with LSTM modules can be used successfully as an approximator for the agent's Q-function to solve the given problem when the partially observed region of system states has a complex structure. The choice of the method of adaptation to the control actions and the external environmental disturbances proposed in the paper satisfies the requirements for the adatation process performance, as well as the requierments for the control processes quality, when there is lack of information about the nature of random control disturbances. The experimental environment, as well as the neural network models was implemented using the Python programming language with TensorFlow library.

Modelling of a Novel Gas Strut Using Neural Networks

2004 ◽  
Author(s):  
B. Gao ◽  
J. Darling ◽  
D. G. Tilley ◽  
R. A. Williams ◽  
A. Bean ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mathematical Model ◽  
Least Square ◽  
Operation Conditions ◽  
The Neural Network ◽  
Wide Range ◽  
Non Linear ◽  
Vehicle Suspension System ◽  
And Control

The strut is one of the most important components in a vehicle suspension system. Since it is highly non-linear it is difficult to predict its performance characteristics using a physical mathematical model. However, neural networks have been successfully used as universal ‘black-box’ models in the identification and control of non-linear systems. This approach has been used to model a novel gas strut and the neural network was trained with experimental data obtained in the laboratory from simulated road profiles. The results obtained from the neural network demonstrated good agreement with the experimental results over a wide range of operation conditions. In contrast a linearised mathematical model using least square estimates of system parameters was shown to perform badly due to the highly non-linear nature of the system. A quarter car mathematical model was developed to predict strut behavior. It was shown that the two models produced different predictions of ride performance and it was argued that the neural network was preferable as it included the effects of non-linearities. Although the neural network model does not provide a good understanding of the physical behavior of the strut it is a useful tool for assessing vehicle ride and NVH performance due to its good computational efficiency and accuracy.

scholarly journals A System for Detecting Anomalous Observations in Data on Healthcare Services

2021 ◽  
Vol 27 (3) ◽  
pp. 356-367
Author(s):  
S. L. Blyumin ◽  
R. V. Scheglevatykh ◽  
A. A. Naydenov ◽  
A. S. Sysoev
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Network Model ◽  
Neural Network Model ◽  
Healthcare Services ◽  
Neural Network Classifier ◽  
The Neural Network ◽  
Incoming Information ◽  
Process Behavior ◽  
The Mathematical Model

A description of the mathematical model of a neural network classifier of data on healthcare in the institutions of the Lipetsk region is given in order to identify atypical (abnormal) records. Anomaly detection refers to the problem of finding data that is inconsistent with some expected process behavior or metric occurring in the system. Due to the large number of inputs to the neural network model, the time it takes to process the incoming information also increases. To assess what factors should be transmitted to the input of the neural network classifier, an approach to the reduction of the neural network model based on sensitivity analysis is proposed. The description of a set of software tools for solving the problem is presented.

Scheduling Problem of Biochemical Analyzer and Exploration of Neural Network-Greedy Algorithm

2020 ◽  
Vol 10 (8) ◽  
pp. 1912-1918
Author(s):  
Xiaohui Huang ◽  
Shuxia Zheng ◽  
Shilong Li ◽  
Jinxiang Wu ◽  
Graham Spence
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Greedy Algorithm ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Scheduling Problem ◽  
The Neural Network ◽  
Biochemical Analyzer ◽  
Analysis System ◽  
The Mathematical Model

The mathematical model of biochemical analysis system was established based on neural network-greedy algorithm. The optimal task scheduling sequence was solved by neural network algorithm. At the same time, the local optimization was obtained by combining greedy algorithm. In this way, the task scheduling problem in biochemical analyzer was transformed into a mathematical problem, and the mathematical model of scheduling algorithm was established. On the platform of MATLAB, eight groups of simulation tests were carried out on the same task scheduling problem using the neural network-greedy scheduling algorithm and the traditional fixedperiod scheduling algorithm. The task-time Gantt charts of the two algorithms were compared under different scheduling orders. The results showed that the average speed of the neural network-greedy algorithm was improved by 31% compared with that of the fixed-period scheduling algorithm. The mathematical model of biochemical analysis system on scheduling problem established by neural network-greedy scheduling algorithm has high efficiency compared with the traditional fixed-period scheduling algorithm.

scholarly journals Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 696
Author(s):  
Eun Ji Choi ◽  
Jin Woo Moon ◽  
Ji-hoon Han ◽  
Yongseok Yoo
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Deep Neural Network ◽  
Mean Squared Error ◽  
Thermal Environment ◽  
The Body ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Body Parts ◽  
Joint Location

The type of occupant activities is a significantly important factor to determine indoor thermal comfort; thus, an accurate method to estimate occupant activity needs to be developed. The purpose of this study was to develop a deep neural network (DNN) model for estimating the joint location of diverse human activities, which will be used to provide a comfortable thermal environment. The DNN model was trained with images to estimate 14 joints of a person performing 10 common indoor activities. The DNN contained numerous shortcut connections for efficient training and had two stages of sequential and parallel layers for accurate joint localization. Estimation accuracy was quantified using the mean squared error (MSE) for the estimated joints and the percentage of correct parts (PCP) for the body parts. The results show that the joint MSEs for the head and neck were lowest, and the PCP was highest for the torso. The PCP for individual activities ranged from 0.71 to 0.92, while typing and standing in a relaxed manner were the activities with the highest PCP. Estimation accuracy was higher for relatively still activities and lower for activities involving wide-ranging arm or leg motion. This study thus highlights the potential for the accurate estimation of occupant indoor activities by proposing a novel DNN model. This approach holds significant promise for finding the actual type of occupant activities and for use in target indoor applications related to thermal comfort in buildings.

scholarly journals Vehicle Sideslip Angle Estimation Using Two Single-Antenna GPS Receivers

2010 ◽  
Author(s):  
Jong-Hwa Yoon ◽  
Huei Peng
Keyword(s):  
Low Cost ◽  
Longitudinal Velocity ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Sideslip Angle ◽  
Gps Receivers ◽  
Single Antenna ◽  
Active Safety Systems ◽  
Safety Systems

Knowing vehicle sideslip angle accurately is critical for active safety systems such as Electronic Stability Control (ESC). Vehicle sideslip angle can be measured through optical speed sensors, or dual-antenna GPS. These measurement systems are costly (∼$5k to $100k), which prohibits wide adoption of such systems. This paper demonstrates that the vehicle sideslip angle can be estimated in real-time by using two low-cost single-antenna GPS receivers. Fast sampled signals from an Inertial Measurement Unit (IMU) compensate for the slow update rate of the GPS receivers through an Extended Kalman Filter (EKF). Bias errors of the IMU measurements are estimated through an EKF to improve the sideslip estimation accuracy. A key challenge of the proposed method lies in the synchronization of the two GPS receivers, which is achieved through an extrapolated update method. Analysis reveals that the estimation accuracy of the proposed method relies mainly on vehicle yaw rate and longitudinal velocity. Experimental results confirm the feasibility of the proposed method.

Prediction and analysis of College Students’ mental health based on BP neural network

2020 ◽  
pp. 1-7
Author(s):  
Tang Yushou Su Jianhuan
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
College Students ◽  
Bp Neural Network ◽  
Structural Characteristics ◽  
Prediction System ◽  
The Neural Network ◽  
Data Processing Method ◽  
Training Samples ◽  
The Mathematical Model

College Students’ mental health is an important part of higher education, so the current research and prediction of College Students’ mental health are of great significance to better solve the problem of College Students’ mental health. Taking a local university as an example, the data from 2011 to 2019 are selected and analyzed. The normalized data processing method is used to assign weights to 11 kinds of factors that affect the health of college students. The training samples of a neural network are selected, and the structural characteristics of the neural network and the artificial neural network toolbox of MATLAB are used to establish the BP based model the mathematical model of the prediction system of College Students’ mental health based on neural network. The results show that the error between the predicted value and the measured value is only 0.88%. On this basis, this paper uses the model to predict the weight of the influencing factors of the mental health status of college students in a local university in 2020 and analyzes the causes of the prediction results, to provide the basis for the current mental health education of college students.

scholarly journals A model-free hull deformation measurement method with time delay compensation

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881069 ◽  
Author(s):  
Ying He ◽  
Xiafu Peng ◽  
Xiaoli Zhang ◽  
Xiaoqiang Hu
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Deformation Measurement ◽  
Estimation Accuracy ◽  
Deformation Model ◽  
Delay Compensation ◽  
The Neural Network ◽  
Time Delay Compensation

Estimation and compensation for hull deformation is an indispensable step for the ship to establish a unified space attitude. The existing hull deformation measurement methods are dependent on the pre-established deformation model, and an inaccurate deformation model will reduce the deformation estimation accuracy. To solve this problem, a hull deformation estimation method without deformation model is proposed in this article, which utilizes the neural network to fit the hull deformation. To train the neural network online, connection weights of the neural network are regarded as system state variables which can be estimated by the Unscented Kalman Filter. Simultaneously, considering the time delay problem of inertial data, a time delay compensation method based on the quaternion attitude matrix is proposed. The simulation results show that the proposed method can obtain high estimation accuracy without any deformation model even when the inertial data are asynchronous.

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