Fuzzy Logic Based Vehicle Stability Enhancement Through Active Rear Steering

2005 ◽  
Author(s):  
A. Ghaffari ◽  
J. Ahmadi ◽  
R. Kazemi
Keyword(s):  
Fuzzy Logic ◽  
Steering System ◽  
Vehicle Stability ◽  
Control Input ◽  
Vehicle Model ◽  
Inference System ◽  
Vehicle Handling ◽  
Stability Enhancement ◽  
Logic Inference ◽  
Yaw Moment

This paper introduces an investigation of a steering intervention system based on active rear steering system (RWS) which uses rear steer angle as control input. The induced yaw moment on the vehicle affects handling states, there by increasing the steering performance. The steering function achieved through RWS can then be used to assist the driver in severe maneuvers that most drivers are not familiar with. Because of the high nonlinearities and uncertainties that exist in vehicle handling behavior a fuzzy logic inference system is developed to explore RWS feasibility and capability. Computer simulations using nonlinear seven degree of freedom vehicle model show the remarkable enhancements of RWS vehicle.

Fuzzy Logic Based Vehicle Stability Enhancement Through Combined Differential Braking and Active Front Steering

2005 ◽  
Author(s):  
J. Ahmadi ◽  
A. Ghaffari ◽  
R. Kazemi
Keyword(s):  
Fuzzy Logic ◽  
Steering System ◽  
Vehicle Stability ◽  
Fuzzy Logic Controllers ◽  
Vehicle Model ◽  
Active Front Steering ◽  
Highly Nonlinear ◽  
Road Condition ◽  
The Stability ◽  
Stability Enhancement

This paper examines the usefulness of a combined differential braking and active front steering system on the stability enhancement of a vehicle. The two manipulated inputs for steering intervention are the added front steer angle and the brake torque, where the later is applied at only one wheel at a time. In this study active front steering controller is designed independent of differential braking controller. Since the yaw and lateral motions are highly nonlinear, two fuzzy logic controllers are constructed to compensate the effects of road condition and parameter variation. Computer simulations using nonlinear seven degree of freedom vehicle model show the strong capability of the combined approach and its relative merit compared to the case that one subsystem is actuated.

Vehicle Stability Analyse with Electronic Power Steering Based on Yaw Rate Feedback

2013 ◽  
Vol 397-400 ◽  
pp. 1351-1356
Author(s):  
Hai Feng Song ◽  
Wei Wei Yang
Keyword(s):  
Control Method ◽  
Integrated Control ◽  
Vehicle Stability ◽  
Vehicle Model ◽  
Steering Angle ◽  
Yaw Rate ◽  
Power Steering ◽  
Rate Feedback ◽  
Yaw Stability ◽  
Yaw Moment

A control method is proposed to improve vehicle yaw stability by the integrated control of yaw moment control. The control strategy using feedback compensator is proposed, which produces direct yaw moment and front steering angle to control yaw rate, by actively controlling the front steering angle, the integrated control system makes the performance of the actual vehicle model follow that of an ideal vehicle model. A experiment is performed at different conditions, the results showed the presented method can effectively control the yaw rate, and at the same time lighten the burden of the driver. Key words: EPS; Yaw rate feedback; Vehicle stability

scholarly journals Application of Soft Computing Models with Input Vectors of Snow Cover Area in Addition to Hydro-Climatic Data to Predict the Sediment Loads

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1481 ◽  
Author(s):  
Waqas Hussan ◽  
Muhammad Khurram Shahzad ◽  
Frank Seidel ◽  
Franz Nestmann
Keyword(s):  
Fuzzy Logic ◽  
Snow Cover ◽  
Sediment Load ◽  
Snow Cover Area ◽  
Inference System ◽  
Sediment Yields ◽  
Fuzzy Logic Inference ◽  
Sediment Loads ◽  
Neuro Fuzzy ◽  
Logic Inference

The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R2 value of 0.85 and 0.74 during the training and testing period, respectively.

A Model of Driver Steering Control Behavior for Use in Assessing Vehicle Handling Qualities

1993 ◽  
Vol 115 (3) ◽  
pp. 456-464 ◽  
Author(s):  
A. Modjtahedzadeh ◽  
R. A. Hess
Keyword(s):  
Steering System ◽  
Manual Control ◽  
Theoretic Model ◽  
Steering Control ◽  
Vehicle Model ◽  
Vehicle Handling ◽  
Analytical Technique ◽  
Handling Qualities ◽  
Control Behavior ◽  
Lane Keeping

A control theoretic model of driver steering control behavior is presented. The resulting model is shown capable of producing driver/vehicle steering responses which compare favorably with those obtained from driver simulation. The model is simple enough to be used by engineers who may not be manual control specialists. The model contains both preview and compensatory steering dynamics. An analytical technique for vehicle handling qualities assessment is briefly discussed. Driver/vehicle responses from two driving tasks evaluated in a driver simulator are used to evaluate the overall validity of the driver/vehicle model. Finally, the model is exercised in predictive fashion in the computer simulation of a lane keeping task on a curving roadway where the simulated vehicle possessed one of three different steering systems: a conventional two-wheel steering system and a pair of four-wheel steering systems.

The Research on 4WS Vehicle Steering Stability by Modeling and Simulation

2011 ◽  
Vol 403-408 ◽  
pp. 3099-3103
Author(s):  
Dai Sheng Zhang ◽  
Jun Jie Huang ◽  
Hao Wang
Keyword(s):  
Modeling And Simulation ◽  
High Speed ◽  
Steering System ◽  
Vehicle Control ◽  
Control Mode ◽  
Vehicle Design ◽  
Vehicle Stability ◽  
Vehicle Model ◽  
Simulation Research ◽  
Theory Research

In order to improve vehicle steering stability, the influence of tire loads and steering system to the vehicle stability is taken into account in this paper, and the 4WS vehicle model is established and modeling and simulation research is carried through with the Matalab/simulink. The results point out the differences and characters of vehicle control mode in low speed and high speed. This model provides a method for 4WS vehicle design, improvement and optimization, and also provides reference for 4WS theory research and test check.

scholarly journals Intelligent Business Decision-Making in Global Organizations via Fuzzy Reasoning Systems

2014 ◽  
pp. 481-507
Author(s):  
Zekâi Şen
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Reasoning ◽  
Management Problem ◽  
Inference System ◽  
Business Decision ◽  
Global Organizations ◽  
Input And Output ◽  
Decision Structure ◽  
Logic Inference

Companies, organizations, governmental departments, and universities need to adopt globalization patterns for their generative survival in dynamic productive outputs. Such outputs are possible only after effective, rational, logical, and systematic treatment of all available input knowledge and information. These inputs mostly have imprecision, uncertainty, vagueness, incompleteness, and missing parts, which together provide a fuzzy arena where an expert is confronted with decision making under a set of conflicting and mutually inclusive vague alternatives. Any uncertain ingredient may be considered as a set of linguistic adjectives attached to the input and output variables so as to refine them into meaningful and less uncertain sub-sets. Logical propositions that combine each sub-set of any input variable to suitable sub-sets of other variables through logical ANDing connectives as precedents are related to a specific sub-set of output as consequent, which constitute fundamental logical rule. These are expert reflections towards management problem solving. The combination of such rules with logical ORing connectives presents linguistically the holistic decision structure of any management system. This chapter presents the essential steps required to achieve decision making under uncertainty for effective management via a fuzzy logic inference system. The basis of fuzzy logic modeling is presented which may be used by different business management experts.

scholarly journals Prioritizing facilities linked to corporate strategic objectives using a fuzzy model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Devin DePalmer ◽  
Steven Schuldt ◽  
Justin Delorit
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Portfolio Management ◽  
Traditional Approach ◽  
Model Performance ◽  
Inference System ◽  
Content Type ◽  
Strategic Objectives ◽  
Fuzzy Logic Inference ◽  
Logic Inference

Purpose Limited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues. Design/methodology/approach A Mamdani fuzzy logic inference system is coupled with a traditional, categorical risk assessment framework to understand a facilities’ consequence of failure and its effect on an organization’s strategic objectives. Model performance is evaluated using the US Air Force’s facility portfolio, which has been previously assessed, treating facility replicability and interruptability as minimization objectives. The fuzzy logic inference system is built to account for these objectives, but as proof of ease-of-adaptation, facility dependency is added as an additional risk assessment criterion. Findings Results of the fuzzy logic-based approach show a high degree of consistency with the traditional approach, though the value of the information provided by the framework developed here is considerably higher, as it creates a continuous set of facility prioritizations that are unbiased. The fuzzy logic framework is likely suitable for implementation by diverse, spatially distributed organizations in which decision-makers seek to balance risk assessment complexity with an output value. Originality/value This paper fills the identified need for portfolio management strategies that focus on prioritizing projects by risk to organizational operations or objectives.

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