Wheel Dynamics Fundamentals for Agile Tire Slippage Modeling and Control

2014 ◽  
Author(s):  
Vladimir V. Vantsevich
Keyword(s):  
Friction Coefficient ◽  
Rolling Resistance ◽  
Resistance Coefficient ◽  
Rate Of Change ◽  
Technical Problems ◽  
Modeling And Control ◽  
Wheel Torque ◽  
Control Decision ◽  
Estimation And Control ◽  
And Control

One of the technical problems in wheel dynamics is to establish and control the relationship between the tire kinematic and force characteristics related to tire slippage and thus to tire-soil power losses and wheel mobility estimation. This problem has been attracting a lot attention from the research community for decades. The electronization of modern vehicles can enhance their performance in complex and severe vehicle-road/terrain environments by implementing agile control decision within the scale of milliseconds. Thus, agility requires new approaches when considering and analyzing the tire slippage process. This paper presents an analysis of the tire slippage process in stochastic terrain conditions for the purpose of agile tire slip modeling, estimation and control. Based on the introduced relations between the rolling radii of the tire, circumferential wheel force/wheel torque, wheel kinematic parameters and tire slippage, a set of agile tire-terrain characteristics is offered in the paper. The proposed characteristics take in consideration the rate of change of the listed parameters and thus allow a user to estimate the agile dynamics of the tire slip and evidence the closeness to the peak friction coefficient and hence estimate potential mobility loss. The characteristics establish relationships between the stochastic peak friction coefficient, rolling resistance coefficient, and wheel kinematic/force parameters. The characteristics are illustrated by computer simulation results in several terrain conditions.

A Sampling Approach to Modeling and Control of a Large-Size Robot Population

2010 ◽  
Author(s):  
Dejan Milutinovic´ ◽  
Devendra P. Garg
Keyword(s):  
Stochastic Process ◽  
Optimal Control ◽  
Close Relation ◽  
Modeling And Control ◽  
Stochastic Sampling ◽  
Large Size ◽  
Continuous Components ◽  
Estimation And Control ◽  
The Individual ◽  
And Control

Motivated by the close relation between estimation and control problems, we explore the possibility to utilize stochastic sampling for computing the optimal control for a large-size robot population. We assume that the individual robot state is composed of discrete and continuous components, while the population is controlled in a probability space. Utilizing a stochastic process, we can compute the state probability density function evolution, as well as use the stochastic process samples to evaluate the Hamiltonian defining the optimal control. The proposed method is illustrated by an example of centralized optimal control for a large-size robot population.

Coefficients of Friction

1949 ◽  
Vol 53 (468) ◽  
pp. 1085-1094 ◽  
Author(s):  
E. C. Pike
Keyword(s):  
Friction Coefficient ◽  
Surface Texture ◽  
Sliding Friction ◽  
Rolling Resistance ◽  
Aircraft Design ◽  
Resistance Coefficient ◽  
Surface Material ◽  
Primary Object ◽  
Different Types ◽  
Sliding Friction Coefficient

SummaryA summary is presented of information collected on coefficients of friction (rolling and sliding) between rubber tyres and road or runway surfaces. Nearly all the data collected are from tests on automobile tyres and are limited to speeds of about 40 m.p.h. In some cases the primary object of the tests was to distinguish between good and bad roads, and not to determine absolute values of friction coefficients.The reasons for the importance of the information for aircraft design use are discussed and distinctions are made between coefficients measured in different ways.The results show the variation of sliding friction coefficient with various parameters at speeds up to 40 m.p.h. There is a set of American tests at speeds up to 110 m.p.h., and some estimates made by the Dunlop Rubber Company up to 120 m.p.h.It is concluded that for sliding friction coeficients differences in surface texture (as distinct from surface material) are significant, and that information can be given only in the form of limits within which the values can be expected to lie.Few results are available on the variation of rolling resistance coefficient, but it is felt that the approximate values quoted for different types of surface are sufficiently accurate.

scholarly journals Development of Online Adaptive Traction Control for Electric Robotic Tractors

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3394
Author(s):  
Idris Idris Sunusi ◽  
Jun Zhou ◽  
Chenyang Sun ◽  
Zhenzhen Wang ◽  
Jianlei Zhao ◽  
...  
Keyword(s):  
Friction Coefficient ◽  
Sliding Mode ◽  
Interpolation Method ◽  
Field Tests ◽  
Soil Disturbance ◽  
Wheel Slip ◽  
Slip Ratio ◽  
Optimal Values ◽  
Estimation And Control ◽  
And Control

Estimation and control of wheel slip is a critical consideration in preventing loss of traction, minimizing power consumptions, and reducing soil disturbance. An approach to wheel slip estimation and control, which is robust to sensor noises and modeling imperfection, has been investigated in this study. The proposed method uses a simplified form of wheels longitudinal dynamic and the measurement of wheel and vehicle speeds to estimate and control the optimum slip. The longitudinal wheel forces were estimated using a robust sliding mode observer. A straightforward and simple interpolation method, which involves the use of Burckhardt tire model, instantaneous values of wheel slip, and the estimate of longitudinal force, was used to determine the optimum slip ratio that guarantees maximum friction coefficient between the wheel and the road surface. An integral sliding mode control strategy was also developed to force the wheel slip to track the desired optimum value. The algorithm was tested in Matlab/Simulink environment and later implemented on an autonomous electric vehicle test platform developed by the Nanjing agricultural university. Results from simulation and field tests on surfaces with different friction coefficients (μ) have proved that the algorithm can detect an abrupt change in terrain friction coefficient; it can also estimate and track the optimum slip. More so, the result has shown that the algorithm is robust to bounded variations on the weight on the wheels and rolling resistance. During simulation and field test, the system reduced the slip from non-optimal values of about 0.8 to optimal values of less than 0.2. The algorithm achieved a reduction in slip ratio by reducing the torque delivery to the wheel, which invariably leads to a reduction in wheel velocity.

scholarly journals Distributed estimation and control of a four-tank process

2021 ◽  
Vol 9 (3) ◽  
pp. 133-142
Author(s):  
Awatef K Ali ◽  
Magdi S Mahmoud
Keyword(s):  
Distributed Model ◽  
Flow Sheet ◽  
Global State ◽  
Communication Structure ◽  
Modeling And Control ◽  
Process Dynamics ◽  
Multivariable Process ◽  
Estimation And Control ◽  
Water Tanks ◽  
And Control

A multivariable process of four interconnected water tanks is considered for modeling and control. The objective of the current study is to design and implement a distributed control and estimation (DEC) for a multivariable four-tank process. Distributed model and inter-nodal communication structure are derived from global state–space matrices, thus combining the topology of plant flow sheet and the interaction dynamics across the plant subunits. Using experimental data, the process dynamics and disturbance effects are modeled. A typical lab-scale system was simulated and the obtained results demonstrated the potential of the DEC algorithm.

Modeling and Control for Plant Dynamics Based on Reinforcement Learning

2009 ◽  
Vol 129 (4) ◽  
pp. 363-367
Author(s):  
Tomoyuki Maeda ◽  
Makishi Nakayama ◽  
Hiroshi Narazaki ◽  
Akira Kitamura
Keyword(s):  
Reinforcement Learning ◽  
Modeling And Control ◽  
Plant Dynamics ◽  
And Control
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