Dynamical Modeling and Neural Network Adaptive Control of Vehicle Suspension

2011 ◽  
Vol 148-149 ◽  
pp. 516-519
Author(s):  
Jun Tao Fei ◽  
Jing Xu
Keyword(s):  
Neural Network ◽  
Suspension System ◽  
The Body ◽  
Vehicle Suspension ◽  
Vertical Acceleration ◽  
Nonlinear Spring ◽  
Passive Suspension ◽  
The Neural Network ◽  
Spring Suspension ◽  
Suspension Model

This paper attempts to establish the vibration control technology based on neural network control. First, the dynamic model of vehicle suspension system is discussed, and the linear passive suspension model and nonlinear spring suspension model of the vertical acceleration are compared. It is shown that the performance of nonlinear spring suspension is better than that of the linear passive suspension model. Because of the great advantages of the neural network in dealing with the nonlinear property, secondly, model reference neural control module is introduced in the suspension system to realize the optimization of the body vertical acceleration. Simulation results demonstrate the effectiveness of the neural network adaptive controller with application to vehicle suspension.

scholarly journals Research on Road Roughness Based on NARX Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yingjie Liu ◽  
Dawei Cui
Keyword(s):  
Neural Network ◽  
The Body ◽  
Identification Accuracy ◽  
Vertical Acceleration ◽  
Plane Model ◽  
The Road ◽  
The Neural Network ◽  
Road Roughness ◽  
Network Identification ◽  
Narx Neural Network

In order to solve the problem of road roughness identification, a study on the nonlinear autoregressive with exogenous inputs (NARX) neural network identification method was carried out in the paper. Firstly, a 7-DOF plane model of vehicle vibration system was established to obtain the vertical acceleration and elevation acceleration of the body, which were set as ideal input samples for the neural network. Then, based on the plane model, with common speed, the road roughness was solved as the ideal output sample of the NARX neural network, and the road roughness of B-level and C-level was identified. The results show that the proposed method has ideal identification accuracy and strong antinoise ability. The relative error of C-level road roughness is larger than that of B-level road roughness. The identified road roughness can provide a theoretical basis for analyzing the dynamic response of expressway roads.

scholarly journals Equivalent Air Spring Suspension Model for Quarter-Passive Model of Passenger Vehicles

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Haider J. Abid ◽  
Jie Chen ◽  
Ameen A. Nassar
Keyword(s):  
Initial Pressure ◽  
Suspension System ◽  
Air Spring ◽  
Passive Suspension ◽  
Road Profile ◽  
Spring Suspension ◽  
Spring System ◽  
Passive Suspension System ◽  
Suspension Model ◽  
System Equivalence

This paper investigates the GENSIS air spring suspension system equivalence to a passive suspension system. The SIMULINK simulation together with the OptiY optimization is used to obtain the air spring suspension model equivalent to passive suspension system, where the car body response difference from both systems with the same road profile inputs is used as the objective function for optimization (OptiY program). The parameters of air spring system such as initial pressure, volume of bag, length of surge pipe, diameter of surge pipe, and volume of reservoir are obtained from optimization. The simulation results show that the air spring suspension equivalent system can produce responses very close to the passive suspension system.

Fuzzy Control and Co-Simulation of Automobile Semi-Active Suspension System Based on SIMPACK and MATLAB

2010 ◽  
Vol 39 ◽  
pp. 50-54 ◽  
Author(s):  
Shao Yi Bei ◽  
Jing Bo Zhao ◽  
Lan Chun Zhang ◽  
Shao Hua Liu
Keyword(s):  
Ride Comfort ◽  
Fuzzy Controller ◽  
Active Suspension ◽  
Simulation Software ◽  
Suspension System ◽  
The Body ◽  
Vertical Acceleration ◽  
Passive Suspension ◽  
Pulse Input ◽  
Car Model

Using the multi-body simulation software SIMPACK as platform, a whole CHANGHE mini-car model was built. A fuzzy controller was adopted based on MATLAB/SIMULINK software to control the full car model. Pulse input running test simulation was carried out under co-simulation of SIMAT. The results showed that compared to passive suspension, with the speed 40km/h, the body vertical acceleration, body pitch angular velocity, standard deviation and peak were respectively decreased by 10.76%, 18.03% and 20.48%, 12.13%. The semi-active suspension system with fuzzy controller had better performance than passive suspension, reduced vibration effectively and improved automotive ride comfort.

A neural network approach to the design of a vehicle's non-linear hybrid suspension system

2002 ◽  
Vol 216 (5) ◽  
pp. 833-838 ◽  
Author(s):  
K N Spentzas ◽  
S A Kanarachos
Keyword(s):  
Neural Network ◽  
Control Function ◽  
Design Method ◽  
Excitation Amplitude ◽  
Suspension System ◽  
Vertical Acceleration ◽  
Neural Network Approach ◽  
The Road ◽  
The Neural Network ◽  
Non Linear

In the following, a design method is presented for non-linear hybrid suspension systems of vehicles based on neural networks. A hybrid suspension system is one that behaves as an active suspension system only when the road excitation amplitude is above a prescribed value. Discontinuous operation of the controller helps to minimize the energy consumed by the actuator. The design targets of our method are the minimization of the vertical acceleration imposed on the passengers as well as the respect of all the design and construction constraints. The neural network used is obtained by a Taylor approximation of the unknown non-linear control function. Because of the existence of numerous local minima of the neural network, an evolutionary algorithm is used to solve the resulting neural network problem.

scholarly journals ANALISA KENYAMANAN PADA KENDARAAN MULTIGUNA PEDESAAN DENGAN PEMODELAN 8 DOF

2019 ◽  
Vol 8 (01) ◽  
pp. 19-23
Author(s):  
Nanda Pranandita
Keyword(s):  
Rural Areas ◽  
Simulation Software ◽  
Suspension System ◽  
Vehicle Suspension ◽  
Vertical Acceleration ◽  
Acceleration Response ◽  
Passive Suspension ◽  
Passive Suspension System ◽  
Simulation Results ◽  
Vehicle Suspension System

Vehicle suspension system is an important part to minimize the vibration of the vehicle caused by road unevenness. Ideal conditions would be difficult to obtain, especially in rural areas with uneven road conditions. Analysis of passive suspension system in this study is expected to explain the level of comfort in riding Rural Multipurpose Vehicles. Full car modelling with 1 DOF riders used in this study, simulated using numerical simulation software. Excitation roads used in the form of a sinusoidal wave with an amplitude of 0.05 m and a wavelength of 0.5 m. Analysis carried out on the comfort of the vertical acceleration response received driver’s head. Based on the simulation results showed that by using a constant speed between 20-40 km / h, the vehicle will be comfortable driving for more than 2.5 hours.

Active Vehicle Suspension Control Using Full State-Feedback Controller

2015 ◽  
Vol 1115 ◽  
pp. 440-445 ◽  
Author(s):  
Musa Mohammed Bello ◽  
Amir Akramin Shafie ◽  
Raisuddin Khan
Keyword(s):  
State Feedback ◽  
Ride Comfort ◽  
Active Suspension ◽  
Suspension System ◽  
Feedback Controller ◽  
Vehicle Suspension ◽  
Passive Suspension ◽  
Full State ◽  
Full State Feedback ◽  
Passive Suspension System

The main purpose of vehicle suspension system is to isolate the vehicle main body from any road geometrical irregularity in order to improve the passengers ride comfort and to maintain good handling stability. The present work aim at designing a control system for an active suspension system to be applied in today’s automotive industries. The design implementation involves construction of a state space model for quarter car with two degree of freedom and a development of full state-feedback controller. The performance of the active suspension system was assessed by comparing it response with that of the passive suspension system. Simulation using Matlab/Simulink environment shows that, even at resonant frequency the active suspension system produces a good dynamic response and a better ride comfort when compared to the passive suspension system.

scholarly journals KOMPARASI ALGORITMA NEURAL NETWORK DAN NAÏVE BAYES UNTUK MEMPREDIKSI PENYAKIT JANTUNG

2019 ◽  
Vol 15 (2) ◽  
pp. 189-194 ◽  
Author(s):  
Hendri Mahmud Nawawi ◽  
Jajang Jaya Purnama ◽  
Agung Baitul Hikmah
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Naive Bayes ◽  
Healthy Lifestyle ◽  
Naïve Bayes ◽  
The Body ◽  
Early Age ◽  
Regular Exercise ◽  
The Neural Network ◽  
Bayes Algorithm

Heart disease is one of the types of deadly diseases whose treatment must be dealt with as soon as possible because it can occur suddenly to the sufferer.  Factors of heart disease that are recognized based on the condition of the body of a sufferer need to be known from an early age so that the risk of possible instant attacks can be minimized or can be overcome in various ways such as a healthy lifestyle and regular exercise that can regulate heart health in the body.  By looking at the condition of a person's body based on sex, blood pressure, age, whether or not a smoker and some indicators that become a person's characteristics of heart disease are described in a study using the Neural Network and Naïve Bayes algorithm with the aim of comparing the level of accuracy to attributes influential to predict heart disease, so the results of this study can be used as a reference to predict whether a person has heart disease or not.

Research on vibration reduction of semi-active suspension system of quarter vehicle based on time-delayed feedback control with body acceleration

2021 ◽  
pp. 146134842110518
Author(s):  
Yong Guo ◽  
Chuanbo Ren
Keyword(s):  
Feedback Control ◽  
Delayed Feedback ◽  
Suspension System ◽  
Delayed Feedback Control ◽  
Vehicle Body ◽  
Vehicle Suspension ◽  
Vertical Acceleration ◽  
Control Parameters ◽  
Vehicle Suspension System ◽  
Time Delayed Feedback

In this paper, the mechanical model of two-degree-of-freedom vehicle semi-active suspension system based on time-delayed feedback control with vertical acceleration of the vehicle body was studied. With frequency-domain analysis method, the optimization of time-delayed feedback control parameters of vehicle suspension system in effective frequency band was studied, and a set of optimization method of time-delayed feedback control parameters based on “equivalent harmonic excitation” was proposed. The time-domain simulation results of vehicle suspension system show that compared with the passive control, the time-delayed feedback control based on the vertical acceleration of the vehicle body under the optimal time-delayed feedback control effectively broadens the vibration absorption bandwidth of the vehicle suspension system. The ride comfort and stability of the vehicle under random road excitation are significantly improved, which provides a theoretical basis for the selection of time-delayed feedback control strategy and the optimal design of time-delayed feedback control parameters of vehicle suspension system.

scholarly journals A Novel Neural Network-Based SINS/DVL Integrated Navigation Approach to Deal with DVL Malfunction for Underwater Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Wanli Li ◽  
Mingjian Chen ◽  
Chao Zhang ◽  
Lundong Zhang ◽  
Rui Chen
Keyword(s):  
Neural Network ◽  
Autonomous Navigation ◽  
Underwater Vehicles ◽  
The Body ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Body Frame ◽  
Short Term ◽  
The Neural Network ◽  
Nonlinear Autoregressive

A navigation grade Strapdown Inertial Navigation System (SINS) combined with a Doppler Velocity Log (DVL) is widely used for autonomous navigation of underwater vehicles. Whether the DVL is able to provide continuous velocity measurements is of crucial importance to the integrated navigation precision. Considering that the DVL may fail during the missions, a novel neural network-based SINS/DVL integrated navigation approach is proposed. The nonlinear autoregressive exogenous (NARX) neural network, which is able to provide reliable predictions, is employed. While the DVL is available, the neural network is trained by the body frame velocity and its increment from the SINS and the DVL measurements. Once the DVL fails, the well trained network is able to forecast the velocity which can be used for the subsequent navigation. From the experimental results, it is clearly shown that the neural network is able to provide reliable velocity predictions for about 200 s–300 s during DVL malfunction and hence maintain the short-term accuracy of the integrated navigation.

Research on the Decoupling Control Algorithm of Full Vehicle Semi-Active Suspension

2012 ◽  
Vol 479-481 ◽  
pp. 1355-1360
Author(s):  
Jian Guo Chen ◽  
Jun Sheng Cheng ◽  
Yong Hong Nie
Keyword(s):  
Differential Geometry ◽  
Control Algorithm ◽  
Active Suspension ◽  
Suspension System ◽  
Decoupling Control ◽  
Vehicle Suspension ◽  
Full Vehicle ◽  
Vibration Attenuation ◽  
Magneto Rheological ◽  
Suspension Model

Vehicle suspension is a MIMO coupling nonlinear system; its vibration couples that of the tires. When magneto-rheological dampers are adopted to attenuate vibration of the sprung mass, the damping forces of the dampers need to be distributed. For the suspension without decoupling, the vibration attenuation is difficult to be controlled precisely. In order to attenuate the vibration of the vehicle effectively, a nonlinear full vehicle semi-active suspension model is proposed. Considering the realization of the control of magneto-rheological dampers, a hysteretic polynomial damper model is adopted. A differential geometry approach is used to decouple the nonlinear suspension system, so that the wheels and sprung mass become independent linear subsystems and independent to each other. A control rule of vibration attenuation is designed, by which the control current applied to the magneto-rheological damper is calculated, and used for the decoupled suspension system. The simulations show that the acceleration of the sprung mass is attenuated greatly, which indicates that the control algorithm is effective and the hysteretic polynomial damper model is practicable.

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