Friction Estimation Research Articles

Online friction parameter estimation for machine tools

Accurate description of frictional phenomena is essential in applications that require high‐accuracy control. Online monitoring of friction parameters in machine tools greatly improves the control accuracy making condition‐based feedforward compensation possible and, at the same time, facilitates equipment wear assessment, which enables efficient scheduling of maintenance. Existing friction estimation methods that use detailed dynamical models offer accurate description of friction phenomena, but often rely on a priori knowledge of the static friction parameters, which have to be identified offline. This article suggests an adaptive estimation strategy suitable for online use while the machine works in its normal production cycle. Smooth approximations are introduced to account for stiction, viscous and bidirectional Coulomb friction in order to make online estimation possible. A parallel architecture is used with two adaptive estimators that segregate the frictional phenomena that dominate in different parts of the motion regime. Stability properties are analyzed and performance is experimentally validated on a single‐axis state‐of‐the‐art industrial test rig.

Multiresolution analysis of three-dimensional (3D) surface texture for asphalt pavement friction estimation

ABSTRACT Maintaining desired pavement friction is an effective way to reduce the crash rate. Pavement texture plays an important role in tire-pavement friction performance. However, the contribution of texture at different depths or wavelengths to friction measurement has not been fully understood. This study conducts a multiresolution analysis using three-dimensional (3D) asphalt pavement texture data to correlate pavement texture to friction measurements. Between 2014 and 2018, multiple rounds of pavement texture and friction data were acquired on two field sites. The top-surface topography of 3D texture data is sliced with eight depths to explore the critical contact depth between pavement texture and tire rubber for friction testing. At each depth, 3D texture data is decomposed into sub-images via discrete wavelet transform to extract texture features at 11 wavelengths ranging from 0.1 mm to 102.4 mm. Subsequently, the wavelet energy of each wavelength is calculated as the texture indicator. Finally, the texture energy matrix at different wavelengths and depths along with the ambient temperature during friction testing are correlated with the measured friction data using cross-validation and step-wise multivariate linear regression. The pavement texture of wavelengths up to 3.2 mm at the top 2.5 mm of top-surface topography is identified as the critical contact zone for pavement-tire interaction.

Optimization-Oriented High Fidelity NFIR Models for Estimating Indicated Torque in Diesel Engines

In this paper, optimization-oriented high fidelity indicated torque models which cover the whole operating regions under both steady-state and transient cycles for heavy-duty vehicles are developed. Two different experiments are performed and their data are merged to be utilized in the training of the models. In the first experiment, all combustion input channels are excited by quadratic chirp signals with different sweeps in their frequency profiles. Different from the first experiment, the engine speed is excited by ramp-hold signals in the second experiment. The estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer are utilized in the indicated torque calculations. In order to model the calculated indicated torque, a nonlinear finite impulse response (NFIR) model with a single layer sigmoid neural network has been designed. A sensitivity analysis is performed by generating several models with different number of input regressors and neurons. Experimental results show that the majority of the models in a selected wide range of the model parameters are validated with fit accuracies higher than 90 % and 85 % on the World Harmonized Stationary Cycle (WHSC) and the World Harmonic Transient Cycle (WHTC), respectively.

Modeling and friction estimation for automotive steering torque at very low speeds

The torque developed in the steering system of an automobile is a result of multiple interactions between a tyre and the roadway surface. Models are well established for high-speed ground vehicle operation where the tyre is rolling rapidly and the dominant source of tire tread stress is deformation due to lateral force on the tyre. In contrast, for situations where the vehicle is operating at low speeds or even steering when stationary, the torque required to turn the wheels is predominantly a function of the steering rate. However, the torque still can be predicted using models of the deformation of the tyre rubber. This paper introduces such a model of the tyre dynamics that considers the low-speed tyre deformation behaviour considering the interacting effects of steering while the tyre is slowly rolling, allowing for the prediction of steering torques during low speed manoeuvring. The physics-based model is validated with steering torque data from an instrumented steer-by-wire vehicle. The validated model also allows for opportunistic friction estimation in certain steady conditions by inversion of the model with the steering rate, steering torque, and vehicle speed as input data.

Safety-extended Explicit MPC for Autonomous Truck Platooning on Varying Road Conditions

Automotive platooning can significantly improve traffic safety and efficiency, but many control challenges need to be solved to function properly under realistic driving conditions. This paper proposes a novel multi-rate explicit model-predictive controller (eMPC) for safe autonomous distributed vehicle platooning in varying road friction conditions. A safety-augmented distributed predictive control formulation ensures safe vehicle spacing versus emergency braking of preceding vehicles given current friction estimates. This complex control problem is carefully formulated into an efficiently parametrized optimization problem realized as eMPC. The resulting platoon shows excellent performance in a complex vehicle dynamics co-simulation validation with low communication and computation demands.

Adaptive Tire-road Friction Estimation Method for Distributed Drive Electric Vehicles Considering Multiple Road Excitations

Adaptive Tire-road Friction Estimation Method for Distributed Drive Electric Vehicles Considering Multiple Road Excitations

A Comparative Study of the Joint Neuro-Fuzzy Friction Models for a Triple Link Rotary Inverted Pendulum

In this paper, Neuro-Fuzzy Friction Estimation Models (NFFEM) are developed to estimate the joint friction coefficients in a Triple Link Rotary Inverted Pendulum (TLRIP) system and compared with an Adaptive Friction Estimation Models (AFEM). The different versions of AFEMs and NFFEMs are generated based on each of the following friction estimation models: Non-Conservative Friction Model (NCFM), Linear Friction Model (LFM), and Non-Linear Friction Model (NLFM). The aim of this study is to obtain joint friction models which depend on both velocity and acceleration in a large range of motion trajectory that involves difficult and sudden large changes. In the proposed NFFEMs, joint velocities and accelerations of the TLRIP are used as the input variables of the Neuro-Fuzzy system trained by using a Radial Basis Function Artificial Neural Network (RBANN). Several experiments are conducted on TLRIP system to verify the NFFEMs. In order to determine the estimation performance of the friction models, total Root Mean Squared Errors (RMSE) between position simulation results obtained from each joint friction model and encoders in the experimental setup are computed. Based on the position RMSEs, the NFFEMs produces much better estimation results than the AFEMs. Among NFFEMs, the neuro-fuzzy nonlinear friction model (NFNLM) gives the best results.

Simple discrete time traction control for a two-wheeled mobile robot

Traction controls usually need dynamic parameters as an approximated reference in the model, such as inertia and friction or heavily computational estimator, such as slip ratio estimator and friction estimator. This paper is shown that the dynamic parameters in the traction controller can be negligible in some cases, internal sensor state variables and gain parameters are enough to retain some traction controllability. The controller is derived from the model following control method (MFC) on a discrete domain. Experiments are performed, in order to evaluate the efficiency of the simplified controller using a two-wheeled mobile robot, by comparing slippage between the position and velocity control scheme with and without the traction control scheme. The experimental results show that the proposed traction control scheme is able to suppress the slip and very simple to implement.

Comparison of experimental procedures of a method for continuous friction measurement in airport pavements

This paper describes a comparison performed between two experimental procedures (with the Grip Tester and the Continuous Friction Measurement Equipment) of the fixed-slip method for longitudinal continuous friction measurement in airport pavements. The measurement framework is described, namely, the experimental testing and the related conformity assessment procedure supported by different friction thresholds. The experimental results of the comparison are presented (raw data, differences between friction estimates and corresponding experimental probability distributions) and its impact on the conformity assessment result is discussed. Conclusions are drawn based on the obtained difference dispersion, instrumental measurement uncertainties and expected difference between the two experimental procedures.

Improvement of friction estimation along wellbores using multi-scale smoothing of trajectories

Drilling monitoring aims at anticipating and detecting any drill string failures during well construction. A key element for the monitoring activity is the estimation of friction along the wellbore trajectory. Friction models require the evaluation of the actual wellbore trajectory. This evaluation is performed applying any of various reconstruction methods available in the industry to discrete deviation measurements. Although all these methods lead to nearly identical bit location, friction estimations are highly dependent on reconstruction methods due to huge dierences in the trajectory derivatives. To control this instability, a new reliable estimation of wellbore friction using a nonlinear trajectory smoothing process is introduced. This process uses a multi-scale approach and a specic nonlinear smoothing through subdivision schemes and their related decimation schemes. Two smoothing processes are compared: one using an interpolatory subdivision operator, and the other, a non-interpolatory subdivision operator. Validation has been performed on a synthetic plane noisy trajectory. The non-interpolatory process provides trajectory derivatives estimate much closer to those of the initial trajectory. Both processes have been applied to a real three-dimensional wellbore trajectory, improving signicantly the friction estimates.

Backdrivability Improvement Method Based on Angular Transmission Error in a High Reduction Gear

The actuators in robot joints are often equipped with reduction gears for increasing the power output. However, a high reduction ratio is required to decrease the backdrivability. In this paper, the backdrivability is improved by installing motor- and load-side encoders in the reduction gear. The proposed method comprises a backdrive assist control, feedforward friction compensation, and estimation and feedback of the load-side disturbance by using a multi-encoder-based disturbance observer. The angular transmission error in the reduction gear is treated as a disturbance that affects the motor. This disturbance is estimated from the motor- and load-side angular accelerations, and provides feedback assistance to the backdrive. The effectiveness of the proposed method is verified through experiments.

Planar motion controller design for a modular mechatronic device with heading compensation

MechaCells are designed as closed, scalable and modular semi-autonomous devices that can be used alone or part of a pack. In this paper, we discuss a locomotion system that uses the reaction force produced by a rotating unbalance that moves in a spherical domain with a steering mechanism. In order to produce the precise motion capability, a multi-loop controller is developed. This controller uses a friction compensation algorithm based on the mathematical model of the locomotion system. To improve the accuracy of tracking, conventional LuGre friction estimation model is extended for rapid directional changes of the MechaCell during planar motion. The linear and rotational acceleration of the device is also included in controller calculations since it affects the locomotion force generated by the unbalanced mass. The resulting control system is validated both with simulations and experiments and the effectiveness of the extended model and the controller is verified. Our results show significant improvement when a detailed friction compensation observer is used in the controller that includes the effect of sudden steering changes for precise path following.

A new approach for estimation of the skin friction in turbulent boundary layer under the adverse pressure gradient conditions

Clauser-chart is commonly regarded as the only indirect method capable for estimation of the local mean skin friction in a turbulent boundary layer based on velocity profile in the overlapping layer, where the logarithmic relation is observed. This approach suffers, however, from the following limitation: it is applicable only for zero and weak pressure gradient flows. However, as is known, in a number of practical configurations the flow has a non-canonical character and is affected, in particular, by the adverse pressure gradient – here airfoils, diffusers and turbines may serve as examples. Hence, with this paper we would like to present a new method which can yield an accurate estimation of wall shear-stress under the strong pressure gradient conditions. In this approach the skin friction can be directly estimated based on the location of intersection point between the inner-scaled streamwise velocity profile and the newly found logarithmic line in the location which covers with the point of the local maximum of large scale motion activity in the outer region of the turbulent boundary layer. It was verified that the proposed method allows for estimation of the skin friction velocity with uncertainty lower than 2.5% for the Clauser–Rota pressure parameter β up to 28 and for 1800 < Reτ < 3900 using data from available literature.

Friction detection from stationary steering manoeuvres

The friction coefficient between the tyres of a vehicle and the roadway is one of the most critical parameters governing a vehicle's motion. However, most techniques for identifying road friction from vehicle motion require the operation of the vehicle in highly dynamic manoeuvres that utilise a significant portion of the tyre grip capacity. Thus, the excitation of dynamics sufficient to provide a reasonable estimate of the friction coefficient could also destabilise the vehicle. This paper presents an alternative approach to friction estimation wherein stationary steering manoeuvres, such as those performed in moments while a vehicle is not moving within a parking lot or at an intersection, may be used to identify the tyre-road friction characteristics prior to higher speed operation. The paper describes a model and results from using a stationary steering process to identify friction. The approach is validated with experimental data from a steer-by-wire test vehicle to illustrate the efficacy of the technique.

Effect of Load-Dependent Friction on the Estimation of Rack Force in Electric Power-Assisted Steering System

The rack force is valuable information for a vehicle dynamics control system, as it relates closely to the road conditions and steering feel. Since there is no direct measurement of rack force in current steering systems, various rack force estimation methods have been proposed to obtain the rack force information. In order to get an accurate rack force estimate, it is important to have knowledge of the steering system friction. However, it is hard to have an accurate value of friction, as it is subject to variation due to operation conditions and material wear. Especially for the widely used column-assisted electric power steering (C-EPAS) system, the load-dependent characteristic of its worm gear friction has a significant effect on rack force estimation. In this paper, a rack force estimation method using a Kalman filter and a load-dependent friction estimation algorithm is introduced, and the effect of C-EPAS friction on rack force estimator performance is investigated. Unlike other rack force estimation methods, which assume that friction is known a priori, the proposed system uses a load-dependent friction estimation algorithm to determine accurate friction information in the steering system, and then a rack force is estimated using the relationship between steering torque and angle. The effectiveness of this proposed method is verified by carsim/simulink cosimulation.

Mobile Light Detection and Ranging for Automated Pavement Friction Estimation

Rapid detection and maintenance of pavement friction is essential for roadway crash prevention. Traditional measurement methods are time-consuming, labor-intensive, and inefficient. This paper proposes a new method to rapidly estimate pavement friction by using a light detection and ranging (LiDAR) sensor. Eight parameters are developed to capture the texture and material information of pavement. The British pendulum number is adopted as the reference of pavement friction with the data collection and processing approaches stated. An ordered logit regression model is utilized to estimate the level of pavement friction, with an average accuracy of 75.86%. The model shows that both textures and material information contribute to pavement friction. Some experimental tests are conducted to explore the potential impact of illumination, showing that lighting and road shading do not affect measurements. The proposed LiDAR-based method is able to assist for rapid, economical, and automatic estimation of pavement friction.

Adaptive Unified Monitoring System Design for Tire-Road Information

Knowledge of the tire-road information is not only very crucial in many active safety applications but also significant for self-driving cars. The tire-road information mainly consists of tire-road friction coefficient and road-tire friction forces. However, precise measurement of tire-road friction coefficient and tire forces requires expensive equipment. Therefore, the monitoring of tire-road information utilizing either accurate models or improved estimation algorithms is essential. Considering easy availability and good economy, this paper proposes a novel adaptive unified monitoring system (AUMS) to simultaneously observe the tire-road friction coefficient and tire forces, i.e., vertical, longitudinal, and lateral tire forces. First, the vertical tire forces can be calculated considering vehicle body roll and load transfer. The longitudinal and lateral tire forces are estimated by an adaptive unified sliding mode observer (AUSMO). Then, the road-tire friction coefficient is observed through the designed mode-switch observer (MSO). The designed MSO contains two modes: when the vehicle is under driving or brake, a slip slope method (SSM) is used, and a recursive least-squares (RLS) identification method is utilized in the SSM; when the vehicle is under steering, a comprehensive friction estimation method is adopted. The performance of the proposed AUMS is verified by both the matlab/simulinkCarSim co-simulation and the real car experiment. The results demonstrate the effectiveness of the proposed AUMS to provide accurate monitoring of tire-road information.

Rapid Road Friction Estimation using Independent Left/Right Steering Torque Measurements

Sensing the friction coefficient between the tyres of an automobile and the road surface is a key challenge in automotive control. Accurate estimation of this parameter enables higher performance and safer operation of vehicles, whether human-operated, assisted or autonomously controlled. Previous approaches have utilised braking or lateral excitation to generate estimates but are limited by the conflict between generating sufficient excitation and preserving ride comfort and efficient operation. Further complicating the problem is the need to provide the friction estimate without adding costly sensors to the vehicle. This paper presents an approach to friction estimation that leverages separate measurements of the left and right steering torques to generate an estimate of the surface friction through direct model inversion. Uncertainty of the method is explored using experimental sensor properties and analysis of the inverted model. The method is shown to be sensitive to reduced friction coefficients and can identify sudden changes before travelling a full contact patch length. Experimental validation of the approach is demonstrated with calibrated wheel force transducers as well as low-cost sensors suitable for incorporation into production vehicles.

Velocity measurement-based friction estimation for railway vehicles running on adhesion limit: swarm intelligence-based multiple models approach

Model-based condition monitoring is an increasingly important area for rail transportation. The key elements of such condition monitoring methodologies are low-cost vehicle sensors and intelligent algorithms. In this study, a swarm intelligence-based multiple models approach is proposed to detect different friction conditions by using velocity measurements of a railway vehicle. In this case of application, estimated parameter is the maximum friction coefficient. Additionally, proposed methodology is tested experimentally by using the measurements taken from a tram wheel test stand. Multiple mathematical models of the test stand are created with different maximum friction coefficients, whereas all initial conditions and other system parameters are same for each model. Therefore, comparison of the output of each model with measurements is considered to interpret the parameter value of the model, which best represents the system, is selected as parameter estimate. Unlike the traditional multiple models approach, a swarm intelligence-based evolution of the models is proposed. Experiments carried out on the test stand reveal that the proposed methodology is promising to be used as an on-board friction condition monitoring tool for railway vehicles with traction. Furthermore, it can be considered to detect weather conditions since friction conditions change due to the weather events such as rain, ice, snowfall, condensation of water droplets, and leaves on the line and it can be used as an auxiliary system for intelligent traction and high adhesion control systems.

Lubricated loaded tooth contact analysis for spur gear pair

Gears are key components to the operation of many machines and mechanisms. However, their presence often affects system efficiency and can lead to noise, vibration and harshness (NVH) issues. Analyses described in open literature study tooth contact neglecting the effect of lubrication. In reality, contact mechanics and lubrication are closely inter-linked, requiring an integrated approach. This paper outlines a combined FEA-based TCA model with a lubricated contact mechanics analysis for real gear pairs measured from coordinate measuring machine (CMM), thus improving the prediction of gear pair efficiency, NVH and durability. An initial dry gear analysis with an estimated constant coefficient of friction in the contact is carried out. The results of this initial analysis provide input data for a subsequent tribological model in order to generate improved estimates of the contact friction for a new TCA. This approach leads to the integration of TCA and lubrication in an iterative manner.