scholarly journals The method of kinematic excitation reconstruction based on measured suspension dynamic responses – experimental verification

2021 ◽  
Vol 1199 (1) ◽  
pp. 012083
Author(s):  
Zbyszko Klockiewicz ◽  
Grzegorz Ślaski ◽  
Hubert Pikosz
Keyword(s):  
Control System ◽  
Experimental Verification ◽  
Tracking Control ◽  
Pid Controller ◽  
Dynamic Responses ◽  
Kinematic Excitation ◽  
Road Profile ◽  
Suspension Dynamics ◽  
Road Excitation ◽  
Unsprung Mass

Abstract The paper presents the method of kinematic road excitation reconstruction based on measured suspension dynamic responses and its reconstruction with use of estimated displacements of unsprung mass as a preliminary approximation of kinematic excitation and tracking control system with a PID controller that allows for faithful reconstruction of unsprung mass accelerations and, in turn, kinematic excitations. The authors performed an experimental verification of the method with use of one axle car trailer and measurements of road profile and acquiring signals of suspension dynamics responses. The signal processing methodology and obtained results are presented for random and determined excitations. The necessary requirements to use the method effectively were defined and its limitations were listed.

The Research of Road Profile Estimation Based on Acceleration Measurement

2012 ◽  
Vol 226-228 ◽  
pp. 1614-1617 ◽  
Author(s):  
Ye Chen Qin ◽  
Ji Fu Guan ◽  
Liang Gu
Keyword(s):  
Dynamic Responses ◽  
The Road ◽  
Physical Measurements ◽  
Power Spectral ◽  
Road Profile ◽  
Road Roughness ◽  
Power Spectral Densities ◽  
Profile Estimation ◽  
The Relationship ◽  
Unsprung Mass

To get the certain response of vehicle during the driving process, it’s necessary to measure the road irregularities. Existing method of gauging the roughness is based on physical measurements and the instrument is installed under the vehicle, which is expensive and will affect the vehicle dynamic responses. This paper shows an easier method to estimate the road roughness by measuring and calculating the power spectral density (PSD) of unsprung mass accelerations. This approach is possible due to the relationship between these two via a transfer function. By comparing the power spectral densities of estimated road and the standard classes, we can classify the current road classes easily. Besides, this paper also shows that it’s feasible to estimate the road profile by calculating the PSD of unsprung mass accelerations directly.

scholarly journals Human tracking control system using Kinect sensors on wheelchair based on Arduino

2020 ◽  
Vol 1436 ◽  
pp. 012003
Author(s):  
H Hamadi ◽  
B Suhendro ◽  
M S Alamsyah ◽  
M Ibrahim
Keyword(s):  
Control System ◽  
Tracking Control ◽  
Human Tracking

Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network

2021 ◽  
pp. 1-11
Author(s):  
Sang-Ki Jeong ◽  
Dea-Hyeong Ji ◽  
Ji-Youn Oh ◽  
Jung-Min Seo ◽  
Hyeung-Sik Choi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Flow Velocity ◽  
Recurrent Neural Network ◽  
Pid Controller ◽  
Short Term Memory ◽  
Ocean Currents ◽  
Ocean Current ◽  
Movement Speed ◽  
Marine Research

In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9].

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