Slip Estimation Research Articles

Single-station single-frequency GNSS cycle slip estimation with receiver clock error increment and position increment constraints

Single-station single-frequency GNSS cycle slip estimation with receiver clock error increment and position increment constraints

Detection and repair of BDS six-frequency cycle slips based on recursive filtering and integer constraint method

The multi-frequency signals of the Beidou system are very helpful for positioning, but in data processing, the detection and repair of multi-frequency cycle slips also face certain challenges. A recursive filtering and integer constraint cycle slip detection and repair method for the Beidou six-frequency is proposed in this paper. This method utilises five sets of combination coefficients of six-frequency and three sets of dual-frequency combinations, and detects and repairs cycle slips through integer constraints of cycle slip estimation. The proposed method has been validated by data with a sampling rate of 1 Hz, and can detect the position and size of cycle slips at various frequencies, whether it is random cycle slips or small cycle slips of equal size less than 2 cycles, with a detection success rate of 100%.

Sequential track fusion in multi-sensor networks of unscented Kalman filters: A case of slip estimation in planetary mobile robots

Accurate wheel slip estimation facilitates Wheeled Mobile Robots (WMRs) with improved localization and traversability monitoring which are crucial to their autonomy in challenging planetary environments. Although distributed track-level fusion offers better computational efficiency, often sensor-level fusion is adopted in slip estimators of planetary rovers. Extending upon our earlier work, we develop a novel explicit track-to-track fusion algorithm for multi-sensor networks of unscented Kalman filters that has immediate application to slip ratio estimation in WMRs. The algorithm demonstrates better consistency compared to the existing fusion techniques since it implements a sub-optimal fusion rule to sequentially combine local tracks. It also saves computational power due to the recursive propagation of cross-covariance matrices based on the statistical linearization technique, instead of performing online optimizations. We prove that this recursion only requires the information of the first and last tracks in a sequence if all local tracks are unbiased. We rigorously study various key properties of the developed fusion algorithm and show its superior level of confidence when compared to the two prominent fusion methods of sequential and batch covariance intersection. The slip ratio estimation in a six-wheel WMR is considered as a case study, where the steerable wheel sets act as a network of sensors. The proposed slip estimator works based on the rigid body kinematics and readings of purely proprioceptive sensors, i.e., an inertial measurement unit and encoders. The slip estimator’s performance is evaluated in a high-fidelity software-in-the-loop simulation environment connecting MATLAB and CM Lab’s Vortex Studio software. In a comparison study that considers four rival strategies, we show the superiority of the proposed fusion method offering a balance between consistency, accuracy, and speed in real-time slip estimations.

A general update rule for Lyapunov‐based adaptive control of mobile robots with wheel slip

SummaryIn this article, we introduce a novel family of Lyapunov‐based adaptive kinematic control laws developed to solve the trajectory tracking problem for a differential‐drive mobile robot under the influence of both longitudinal and lateral wheel slip. Each adaptive controller in this family is constructed by augmenting a nonadaptive nominal controller, originally designed for the slip‐free case, with an update rule capable of estimating the longitudinal slip. In the absence of lateral slip and under constant longitudinal slip, we establish the convergence of the trajectory tracking error to zero and, assuming a persistent excitation condition, we also demonstrate the convergence of the slip estimate error to zero. When lateral slip is present, we analyse a particular control law from our family of adaptive controllers. This law ensures the trajectory tracking error is uniformly ultimately bounded around the origin, demonstrating the robustness of our adaptive control scheme in dealing with time‐varying longitudinal and lateral slip. The validity of our approach is assessed through comprehensive numerical simulations.

Adaptive robust control with slipping parameters estimation based on intelligent learning for wheeled mobile robot

The widespread use of wheeled mobile robots (WMRs) in many fields has created new challenges. A critical issue is wheel slip, which, if not accurately determined and controlled, causes instability and deviation from the robot’s path. In this paper, an intelligent approach for estimating the longitudinal and lateral slip of wheels is proposed that can effectively compensate for the negative effects of slippage. The proposed algorithm relies on three regression networks to estimate the longitudinal slip ratio of the right and left wheels and sideslip angle on terrains with different friction coefficients. The datasets collected during tests on different surfaces with various maneuvers are used to train the artificial neural networks (ANNs). A developed dynamic model of a WMR considering wheel slip and modified traction force is presented. The adaptive robust controller, based on sliding mode control (SMC), is introduced to deal with the problems related to slipping, unknown uncertainties, and disturbances. The simulation results demonstrate that the presented controller has better performance than SMC in handling external disturbances and uncertainties, which leads to reduction in tracking error and faster convergence to zero. The proposed controller with an intelligent slip estimator, has been applied to a four-wheel mobile robot to demonstrate its effectiveness and feasibility. The high accuracy of slip estimation in the mentioned intelligent algorithm has resulted in the presented method being on average 26% more effective in reducing the tracking error than the control method without slip compensation in each test for circular trajectory.

Adaptive Skid-Steering Control Approach for Robots on Uncertain Inclined Planes with Redundant Load-Bearing Mobility.

Climbing manufacturing robots can create a revolutionary manufacturing paradigm for large and complex components, while the motion control of climbing manipulation-oriented robots (CMo-Rs) is still challenging considering anti-slippage problems. In this study, a CMo-R with full-scenery climbing capability and redundant load-bearing mobility is designed based on magnetic adsorption. A four-wheel kinematic model considering the slipping phenomenon is established. An adaptive kinematic control algorithm based on slip estimation using Lyapunov theory is designed for uncertain inclined planes. For comparison, the traditional PID-based algorithm without slip consideration is implemented as well. Numeric simulations are conducted to tackle the trajectory tracking problems for both circular and linear trajectories on the horizontal plane (HP), 50° inclined plane (50° IP), 60° inclined plane (60° IP), and vertical plane (VP). The results prove that our approach achieves better tracking accuracy. It demonstrated applicability in various climbing scenarios with uncertain inclined planes. The results of experiments also validate the feasibility, applicability, and stability of the proposed approach.

Simultaneous Estimation of Vehicle Sideslip and Roll Angles Using an Event-Triggered-Based IoT Architecture

In recent years, there has been a significant integration of advanced technology into the automotive industry, aimed primarily at enhancing safety and ride comfort. While a notable proportion of these driver-assist systems focuses on skid prevention, insufficient attention has been paid to addressing other crucial scenarios, such as rollovers. The accurate estimation of slip and roll angles plays a vital role in ensuring vehicle control and safety, making these parameters essential, especially with the rise of modern technologies that incorporate networked communication and distributed computing. Furthermore, there exists a lag in the transmission of information between the various vehicle systems, including sensors, actuators, and controllers. This paper outlines the design of an IoT architecture that accurately estimates the sideslip angle and roll angle of a vehicle, while addressing network transmission delays with a networked control system and an event-triggered communication scheme. Experimental results are presented to validate the performance of the IoT architecture proposed. The event-triggered scheme of the IoT solution is used to decrease data transmission and prevent network overload.

Erratum to “Sideband Harmonics Identification and Application for Slip Estimation of Induction Motors Based on a Self-Adaptive Wiener Filter”

Erratum to “Sideband Harmonics Identification and Application for Slip Estimation of Induction Motors Based on a Self-Adaptive Wiener Filter”

Data Driven Control and slip estimation for Agricultural Tracked Vehicles

This paper focusses on the path following problem for autonomous Unmanned Ground Vehicles developed for precision agriculture. Such vehicles typically face rough terrain and narrow navigating spaces, so reliability of odometry is largely reduced. In this context, slip estimation/accommodation becomes crucial for localization and navigation of vehicles. In this work, a control architecture is presented coupling MIMO Data-Driven Control and Sliding Mode Control, with the inclusion of a fault estimation mechanism aimed at mitigating the actuators’ loss of effectiveness associated to slipping. The supporting theoretical development makes use of previous results available in the Model Free Adaptive Control framework, which have been extended in order to deal with the path following problem for the vehicle. On the basis of extensive simulations, the proposed control policy is shown able to strengthen the recovery capability and robustness inherently owned by the original adaptive mechanism and control laws.

Slip Estimation Using Variation Data of Strain of the Chassis of Lunar Rovers Traveling on Loose Soil

The surface of the Moon and planets have been covered with loose soil called regolith, and there is a risk that the rovers may stack, so it is necessary for them to recognize the traveling state such as its posture, slip behavior, and sinkage. There are several methods for recognizing the traveling state such as a system using cameras and Lidar, and they are used in real exploration missions like Mars Exploration Rovers of NASA/JPL. When a rover travels and travels across loose soil with steep slopes like a side wall of a crater on the lunar surface, the rover has side slipping. It means that its behavior makes the rover slip down to the valley direction. Even if this detection uses sensors like a camera and Lidar or other controlling systems like SLAM (Simultaneous Localization and Mapping), it would be too difficult for the rover to avoid slipping down to valley direction, because it is not able to detect the traction or resistance given from ground by individual wheel of the rover, as the traction of individual wheel of the rover is not clear. This means that the movement of the rover appeared by integrating the traction of all wheels mounted on the rover. Even if the localization by sensors is carried out, the location would be the location after slipping down. This is because when traveling on unstable ground, the driving force of each individual wheel cannot be accurately predicted, and the sum of the driving force of all wheels is the motion of the rover, which is detected after the position changes. Therefore, if the rover obtains information on the traction of each wheel, its maneuver to change its posture would work sooner and it would be able to travel more efficiently than in a state without that information. Because the onboard computer of rovers can identify their location and state from the information of the traction of each wheel, they can decide the next work carefully and in detail. From these tasks, we focused on the intrinsic sensation of a biological function like a human body and aimed to develop a system that recognizes the traveling state (slip condition) from the shape deformation of the chassis. In this study, we experimentally verified the relationship between the change in strain, which is the amount of deformation acting on the chassis, and the traveling state while the wheel is traveling. From the experimental results, we confirmed that the strain in the chassis was displaced dynamically and that the strain changed oscillatory while the wheel was traveling. In addition, based on the function of muscle spindles as mechanoreceptors, we discussed two methods of analyzing strain change: nuclear chain fiber analysis and nuclear bag fiber analysis. These analyses mean that the raw data of the strain are updated to detect the characteristic strain elements of a chassis while the wheel is traveling through loose soil. Eventually, the slipping state could be estimated by updating the data of a lot of strain raw data, and it was confirmed that the traveling state could be detected.

Accuracy of Finite Fault Slip Estimates in Subduction Zone Regions With Topographic Green's Functions and Seafloor Geodesy

AbstractUntil recently, the lack of seafloor geodetic instrumentation and the use of unrealistically simple, half‐space based forward models have resulted in poor resolution of near‐trench slip in subduction zone settings. Here, we use a synthetic framework to investigate the impact of topography and geodetic data distribution on coseismic slip estimates in various subduction zone settings. We calculate surface displacements in two synthetic topographic domains that have topography similar to that of Chile and Japan, respectively. We then attempt to image target slip distributions by using a Bayesian approach to solve for slip with two sets of Green's functions—one that accounts for topography and one that does not—and five sets of 50 or more observation points selected from the synthetic surface displacements. Three of these sets of observation points are entirely onland, and two include 5–10 seafloor geodetic sites. We find that the use of topographic Green's functions always improves inferred slip models, and with seafloor geodetic data, it enables an almost perfect recovery of a target slip model, even in the near‐trench region. Critically, our results demonstrate that it would be impossible for non‐topographic Green's functions to properly recover the true slip distribution, particularly in the near‐trench region. We also perform a parameter study with approximately 4,000 slip models estimated using a least‐square approach, and find that topographic Green's functions yield significantly more accurate slip models in cases where good data (well distributed and reasonably dense) are available, even in the absence of seafloor geodetic sites.

Instantaneous optimum wheel slip estimation of anti-lock braking system based on extremum seeking algorithm and fuzzy method

The anti-lock braking system (ABS) adjusts the longitudinal wheel slip at its optimum value to achieve the maximum braking forces. The highest braking force capacity happens at a specific slip value and depends on the friction coefficient between the tire and the road, vertical tire force, and vehicle speed. Hence, using a fixed value for the desired longitudinal slip is not appropriate. To solve this problem, the instantaneous optimum wheel slip is determined via the sliding mode-based extremum seeking algorithm in combination with the fuzzy method to achieve the maximum possible brake deceleration. Then the nonlinear prediction-based controller is designed to find the braking torque by adjusting the longitudinal slip in the calculated desired value. In addition, a nonlinear half-vehicle model considering pitch dynamics is developed and validated with the Carsim software. The main contribution of the present work involves the combination of the optimal nonlinear predictive control method with the fuzzy extremum seeking algorithm to design a wheel slip controller. Additionally, the pitch dynamics has been taken into account in the design of the control system. The performance of the designed control system is investigated through conducted simulations in Matlab/Simulink software environment. The obtained results show an enhancement in the braking performance along with a considerable reduction in the stopping distance.

Estimation of wheel slip in 2WD mode for an agricultural tractor during plowing operation using an artificial neural network

As artificial neural networks (ANNs) have been shown to be precise and reliable in supporting the field of artificial intelligence technology, agricultural scientists have focused on employing ANN for agricultural applications. The ANN can be an effective alternative for evaluating agricultural operations. The intended aim of this investigation was to employ both ANN and multiple linear regression (MLR) to develop a model for determining the rear wheel slip of an agricultural tractor in two-wheel drive (2WD) mode during plowing operations. The output parameter of the models was tractor rear wheel slip. The training data were collected from filed experiments using chisel, moldboard, and disk plows. The plows were operated under different conditions of soil texture, plowing depth, soil moisture content, and plowing speed. All data were acquired during field experiments in two soil textures (clay and clay loam textures). The training dataset was comprised of 319 data points, while 65 data points were employed to test both ANN and MLR models estimation capability. The ANN model with a backpropagation training algorithm was created using the commercial Qnet2000 software by changing its topology and related parameters. The best ANN model possessed a topology of 7-20-1. The estimated tractor rear wheel slip using the testing dataset displayed strong agreement with measured tractor rear wheel slip with the coefficient of determination (R2) value of 0.9977. The results definitely illustrated that the ANN model was capable of defining the correlation between the inputs and rear wheel slip. The outcomes suggest that the established ANN model is trustworthy in predicting the tractor rear wheel slip for an agricultural tractor in 2WD mode during the tillage process compared to MLR models. This study provides a useful tool for management of tillage implements during field operations.

Quantitative determination of fault slip magnitude: a review

In geology, a fault is a rock fracture with perceptible relative displacement of the opposite sides of the fracture. Quantitatively obtaining the activity history of faults depends mainly on the estimation of fault slip and the study of markers, which can be used to understand and analyze the tectonic evolution of the faulted regions. Fault slip is calculated by restoring points that were originally adjacent before the deformation, those points are named piercing points. In this paper, we review some published methods to determine fault slip, using: (1) the offset of contours on structural contour maps; (2) offset on seismic reflection sections; (3) a known slip vector (fault striae) and one marker; (4) two known markers. Cases (3) and (4) are commonly applied to field work and geological maps.

Nonintrusive Motor Current Signature Analysis

Motor current signature analysis (MCSA) techniques measure slip-related current signals in the supply lines of a squirrel-cage induction machine (SCIM) for slip estimation, fault detection, and motor diagnostics. In a traditional setup for MCSA, current sensors directly measure the machine currents and are usually installed physically close to the machine of interest, e.g., current transducers clamped about the machine stator leads. For grid-connected SCIMs, the slip-related current signals propagate through the power system. Thus, a single aggregate power monitor can potentially perform MCSA for a collection of SCIMs powered by a common electrical service, referred to in this article as nonintrusive MCSA. Current division and system impedances (source impedance, load impedances, and machine parameters) determine the applicability of nonintrusive MCSA. This article demonstrates measurement hardware, modeling, and experimental results for nonintrusive MCSA. Analysis and results investigate the current-source model of a grid-connected SCIM and its interaction with the system impedances. Applied to a distribution system with multiple machines, the techniques inform when nonintrusive MCSA is possible for collections of SCIMs and equivalent to traditional MCSA.

Sideband Harmonics Identification and Application for Slip Estimation of Induction Motors Based on a Self-Adaptive Wiener Filter

Sideband Harmonics Identification and Application for Slip Estimation of Induction Motors Based on a Self-Adaptive Wiener Filter

In situ slip estimation for mobile robots in outdoor environments

AbstractAccounting for wheel–terrain interaction is crucial for navigation and traction control of mobile robots in outdoor environments and rough terrains. Wheel slip is one of the surface hazards that needs to be detected to mitigate against the risk of losing the robot's controllability or mission failure occurring. The open problems in the Terramechanics field addressed are (1) the need for in situ wheel‐slippage estimation in harsh environments using low‐cost/power and easy to integrate sensors, and (2) removing the need for prior information of the soil, which is not always available. This paper presents a novel slip estimation method that utilizes only two proprioceptive sensors (IMU and wheel encoder) to estimate the wheel slip using deep learning methods. It is experimentally shown to be real‐world feasible in outdoor, uneven terrains without prior soil information assumptions. Comparison with previously used machine learning algorithms for continuous and discrete slip estimation problems show more than 9% and 14% improvement in estimation performance, respectively.

Impact of Effective Tire Radius on Wheel Slip Estimation and Antilock Brake System Performance of Heavy Road Vehicles

The wheel slip ratio is a function of the longitudinal speed of the wheel center, angular wheel speed, and Effective Tire Radius (ETR). The longitudinal speed of the wheel center and angular wheel speed can be calculated/estimated using an accelerometer and wheel speed sensor. However, ETR has uncertainties owing to variation in vehicle mass, air pressure in the tire, and dynamic load transfer. This article firstly demonstrates the sensitivity of ETR on wheel slip estimation using a Fixed Radius Recursive Least Squares (FR-RLS) estimator and analyzes its effects on Antilock Brake System (ABS) performance. Secondly, it proposes a Radius Searching RLS (RS-RLS) wheel slip estimator that mitigates the drift in estimates, maintains vehicle stability, and improves ABS performance. The proposed method was evaluated on a Hardware-in-Loop system with IPG TruckMaker. The RS-RLS estimator based ABS algorithm was compared against three FR-RLS estimator based ABS algorithms having different values of ETR and was found to have one order of magnitude lower mean absolute error.

Mechanical Models of Fault-Slip Rates in the Transverse and Peninsular Ranges, California

AbstractThe Transverse and Peninsular Ranges of Southern California host a geometrically complex network of seismically active faults with a range of slip senses. Here, we present 3D mechanical models of this region that are driven by the total Pacific-North American plate motion and slip on the San Andreas, San Jacinto, and Garlock faults. Based on these boundary conditions, we solve for the full 3D distribution of slip rates on 83 faults and compare model slip-rate predictions to long-term slip-rate data from Uniform California Earthquake Rupture Forecast version 3 (UCERF3) as well as individual geologic point-based estimates of long-term slip. About 46/68 (68%) model-predicted average fault-slip rates are within the UCERF3 slip-rate ranges with a root mean squared error (RMSE) of 1.03 mm/yr to the nearest (upper or lower) UCERF3 limit. The largest slip-rate discrepancies occur on the San Gabriel, Pine Mountain, and Big Pine faults, which may be presently inactive. We find that removing these three faults (i.e., assuming they are inactive) results in an improvement in all goodness-of-fit metrics with 47/63 (75%) UCERF3 slip rates fit with an RMSE of 0.40 mm/yr. We also compare this same model to existing point-based long-term slip-rate estimates and fit 39/69 (60%) with an RMSE of 0.79 mm/yr. The ability for the model to fit independent slip-rate data implies that strike slip along the “Big Bend” of the San Andreas fault (in conjunction with slip on the Garlock and San Jacinto faults) along with total plate motion is sufficient to reproduce both reverse-slip rates throughout the Transverse ranges and strike-slip rates in the Peninsular Ranges regions without additional driving forces needed. Overall, the models provide slip rates and distributions for 83 faults, including faults that currently do not have geologic slip-rate estimates and/or may not produce interseismic deformation.

How do differences in interpreting seismic images affect estimates of geological slip rates?

Abstract. Uncertainties of geological structural geometry constructed based on seismic reflections can stem from data acquisition, processing, analysis, or interpretation. Uncertainties arising from structural interpretations and subsequent estimates of geological slip have been particularly less quantified and discussed. To illustrate the implications of interpretation uncertainties for seismic potential and structural evolution, I use an example of a shear fault-bend fold in the central Himalaya. I apply a simple solution from the kinematic model of shear fault-bend folding to resolve the geological input slip of given structure and then compare the result with a previous study to show how differences in structural interpretations could impact dependent conclusions. The findings show that only a little variance in interpretations owing to subjectivity or an unclear seismic image could yield geological slip rates differing by up to ∼ 10 mm yr−1, resulting in significantly different scenarios of seismic potential. To reduce unavoidable subjectivity, this study also suggests that the epistemic uncertainty in raw data should be included in interpretations and conclusions.