scholarly journals Lateral & vertical dynamic analysis of a three-wheeled motorbike by the planar single track & 3d vertical dynamic models

2015 ◽  
Vol 18 (4) ◽  
pp. 77-84
Author(s):  
Nhan Huu Tran ◽  
Lam Quang Tran ◽  
Duc Tran ◽  
Hung Dinh Nguyen
Keyword(s):  
Theoretical Calculation ◽  
Dynamic Model ◽  
Degrees Of Freedom ◽  
Dynamic Models ◽  
Experimental Measurements ◽  
Single Track ◽  
Vehicle Dynamic ◽  
Calculation Methods ◽  
Dynamic Features ◽  
Vehicle Dynamic Model

To be able to analyze the dynamic features comprehensively and more fully in both the lateral and vertical cases for a threewheeled motorbike (TWM), which have been designed and manufactured by the same group of authors and based on to conduct design improvements, the planar vehicle dynamic model (single track) with 03 degrees of freedom (03-DOF) & the vertical dynamic model with 06 degrees of freedom (06-DOF) have been employed. The parameters used in the calculations are based on existing designs from realistic models manufactured through the combination of experimental measurements and theoretical calculation methods empirically. The lateral dynamic calculated results were based on to investigate the dynamic stability when cornering or steering of a 03-wheeled motorbike. In addition, dynamic calculated results were analyzed also in the frequency domain and basec on to help improve the design featurers with more comfortable and safer.

Design of Path Tracking Controller for Autonomous Vehicles Through Bias Learning of Vehicle Dynamic Models Under Environmental Uncertainty

2021 ◽  
Author(s):  
Lichuan Ren ◽  
Zhimin Xi
Keyword(s):  
Dynamic Model ◽  
Autonomous Vehicles ◽  
Dynamic Models ◽  
Control Strategies ◽  
Tracking Error ◽  
Environmental Uncertainty ◽  
Path Tracking ◽  
Vehicle Dynamic ◽  
Vehicle Dynamic Model ◽  
Bias Learning

Abstract Path tracking error control is an important functionality in the development of autonomous vehicles when a collision-free path has been planned. Large path tracking errors could lead to collision or even out of the control of the vehicle. Vehicle dynamic models are used to minimize the vehicle path tracking error so that control strategies can be designed under different scenarios. However, the vehicle dynamic model may not truly represent the actual vehicle dynamics. Furthermore, the nominal parameter employed in the vehicle dynamic model cannot represent actual operating conditions of the vehicle under environmental uncertainty. This paper presents a learning-based bias modeling method to improve the fidelity of any baseline vehicle dynamics model so that effective path tracking controller design can be achieved through a low fidelity but high-efficiency vehicle dynamic model with the aid of a few experiments or high fidelity simulations. The state-of-the-art of machine learning models, such as Gaussian process (GP) regression, recurrent neural network (RNN), and long short-term memory (LSTM) network, are employed for bias learning and comparison. A high-fidelity vehicle simulator, CARLA, is employed to collect virtual test data and demonstrate the effectiveness of the proposed bias-learning based control strategies under environmental uncertainty.

Co-Simulation of Power Steering Control and Vehicle Dynamic Responses

2001 ◽  
Author(s):  
Gene Y. Liao
Keyword(s):  
Dynamic Model ◽  
General Purpose ◽  
Dynamic Responses ◽  
Steering Wheel ◽  
Vehicle Dynamic ◽  
Steering Control ◽  
Integrated Simulation ◽  
Full Vehicle ◽  
Power Steering ◽  
Vehicle Dynamic Model

Abstract Many general-purpose and specialized simulation codes are becoming more flexible which allows analyses to be carried out simultaneously in a coupled manner called co-simulation. Using co-simulation technique, this paper develops an integrated simulation of an Electric Power Steering (EPS) control system with a full vehicle dynamic model. A full vehicle dynamic model interacting with EPS control algorithm is concurrently simulated on a single bump road condition. The effects of EPS on the vehicle dynamic behavior and handling responses resulting from steer and road input are analyzed and compared with proving ground experimental data. The comparisons show reasonable agreement on tie-rod load, rack displacement, steering wheel torque and tire center acceleration. This developed co-simulation capability may be useful for EPS performance evaluation and calibration as well as for vehicle handling performance integration.

Development of a reduced dynamic model for comfort evaluation of rail vehicle systems

2016 ◽  
Vol 230 (4) ◽  
pp. 489-504 ◽  
Author(s):  
Mortadha Graa ◽  
Mohamed Nejlaoui ◽  
Ajmi Houidi ◽  
Zouhaier Affi ◽  
Lotfi Romdhane
Keyword(s):  
Dynamic Model ◽  
Degrees Of Freedom ◽  
Dynamic Models ◽  
Railway Vehicle ◽  
Passenger Comfort ◽  
Rail Vehicle ◽  
Vehicle System ◽  
Comfort Evaluation ◽  
Complex Models ◽  
Reduced Dynamic

In this paper, an analytical reduced dynamic model of a rail vehicle system is developed. This model considers only 38 degrees of freedom of the rail vehicle system. This reduced model can predict the dynamic behaviour of the rail vehicle while being simpler than existing dynamic models. The developed model is validated using experimental results found in the bibliography and its results are compared with existing more complex models from the literature. The developed model is used for the passenger comfort evaluation, which is based on the value of the weighted root mean square acceleration according to the ISO 2631 standard. Several parameters of the system, i.e., passenger position, loading of the railway vehicle and its speed, and their effect on the passenger comfort are investigated. It was shown that the level of comfort is mostly affected by the speed of the railway vehicle and the position of the seat. The load, however, did not have a significant effect on the level of comfort of the passenger.

scholarly journals Computational Modeling: Human Dynamic Model

2021 ◽  
Vol 15 ◽  
Author(s):  
Lijia Liu ◽  
Joseph L. Cooper ◽  
Dana H. Ballard
Keyword(s):  
Dynamic Model ◽  
Degrees Of Freedom ◽  
Dynamic Models ◽  
Movement Analysis ◽  
Human Movement ◽  
Uncontrolled Manifold ◽  
Human In The Loop ◽  
Joint Forces ◽  
Internal Joint ◽  
Movement Function

Improvements in quantitative measurements of human physical activity are proving extraordinarily useful for studying the underlying musculoskeletal system. Dynamic models of human movement support clinical efforts to analyze, rehabilitate injuries. They are also used in biomechanics to understand and diagnose motor pathologies, find new motor strategies that decrease the risk of injury, and predict potential problems from a particular procedure. In addition, they provide valuable constraints for understanding neural circuits. This paper describes a physics-based movement analysis method for analyzing and simulating bipedal humanoid movements. The model includes the major body segments and joints to report human movements' energetic components. Its 48 degrees of freedom strike a balance between very detailed models that include muscle models and straightforward two-dimensional models. It has sufficient accuracy to analyze and synthesize movements captured in real-time interactive applications, such as psychophysics experiments using virtual reality or human-in-the-loop teleoperation of a simulated robotic system. The dynamic model is fast and robust while still providing results sufficiently accurate to be used to animate a humanoid character. It can also estimate internal joint forces used during a movement to create effort-contingent stimuli and support controlled experiments to measure the dynamics generating human behaviors systematically. The paper describes the innovative features that allow the model to integrate its dynamic equations accurately and illustrates its performance and accuracy with demonstrations. The model has a two-foot stance ability, capable of generating results comparable with an experiment done with subjects, and illustrates the uncontrolled manifold concept. Additionally, the model's facility to capture large energetic databases opens new possibilities for theorizing as to human movement function. The model is freely available.

Investigation on a semi-active hydraulic damping strut to reduce vehicle in situ shift vibration

2019 ◽  
Vol 26 (1-2) ◽  
pp. 3-18
Author(s):  
Dao-Yong Wang ◽  
Wen-Can Zhang ◽  
Xia-Guang Zeng
Keyword(s):  
Dynamic Response ◽  
Dynamic Model ◽  
Automatic Transmission ◽  
Degree Of Freedom ◽  
Vehicle Dynamic ◽  
Response Evaluation ◽  
Evaluation Indexes ◽  
Engine Mount ◽  
Vehicle Dynamic Model

In order to reduce the shock and vibration caused by torque disturbance of the gearbox in vehicles equipped with automatic transmission in the process of in situ shift, a novel semi-active hydraulic damping strut is introduced in the powertrain mounting system. The dynamic response evaluation indexes of vehicle in situ shift are put forward, and a 13-degree of freedom vehicle dynamic model including the semi-active hydraulic damping strut is established. The optimized dynamic characteristic parameters are acquired according to the principle of sharing force and the 13-degree of freedom vehicle dynamic model. The dynamic response evaluation indexes with and without the semi-active hydraulic damping strut are calculated using the 13-degree of freedom vehicle dynamic model in the process of in situ shift, and the calculation results show that the vibration of a vehicle can be reduced by the introduction of a semi-active hydraulic damping strut. Experiments are carried out to analyze the vibration response of the vehicle with and without a semi-active hydraulic damping strut, and the results show that the shock and vibration of the vehicle are reduced by introducing the semi-active hydraulic damping strut. The theoretical calculation values of active-side acceleration of the engine mount and torque strut are consistent with the experimental values, which show that the 13-degree of freedom vehicle dynamic model is reasonable.

Lateral robust iterative learning control for unmanned driving robot vehicle

2020 ◽  
Vol 234 (7) ◽  
pp. 792-808
Author(s):  
Shuhua Su ◽  
Gang Chen
Keyword(s):  
Dynamic Model ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Steering System ◽  
Path Tracking ◽  
Iterative Learning ◽  
Control Law ◽  
Vehicle Dynamic ◽  
Vehicle Dynamic Model

In order to achieve stable steering and path tracking, a lateral robust iterative learning control method for unmanned driving robot vehicle is proposed. Combining the nonlinear tire dynamic model with the vehicle dynamic model, the nonlinear vehicle dynamic model is constructed. The structure of steering manipulator of unmanned driving robot vehicle is analyzed, and the kinematics model and dynamics model of steering manipulator of unmanned driving robot vehicle are established. The structure of vehicle steering system is analyzed, and the dynamic model of vehicle steering system is established. Vehicle steering angle model is established by taking vehicle path tracking error and vehicle yaw angle error as input. Combining with the typical iterative learning control law, the robust term is added to the control law, and a robust iterative learning controller for steering manipulator system of unmanned driving robot vehicle is designed. The proposed controller’s stability and astringency are proved. The effectiveness of the proposed method is verified by comparing it with other control methods and human driver simulation tests.

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