Analysis of the Holonomic Constraint in the Whipple Bicycle Model (P267)

Abstract Platooning/car following has been considered as a promising approach for improving vehicle efficiency due to the reduction of aerodynamic force when closely following a pilot vehicle. However, safety is a major concern in the close car platooning/following. This paper investigates the minimum inter-vehicle distances required for a passenger vehicle to safely travel behind a heavy-duty truck with three different types of emergency maneuvers. The three emergency maneuvers considered are braking only, steering only, and braking then steering, where steering refers to a single lane change maneuver. Numerical analysis is conducted for deriving the clearance space in the braking only scenario. In addition, simulations are conducted in MATLAB/Simulink, using a bicycle model for the vehicle dynamics, to examine the minimum safe following distance for the other two scenarios. The simulation results show that, for initial vehicle speeds greater than 8 m/s, a lane change maneuver requires the shortest safety distance. Braking followed by lane changing usually requires the largest minimum safety distance.

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Non-holonomic Constraint

Encyclopedia of Neuroscience ◽

10.1007/978-3-540-29678-2_4019 ◽

2008 ◽

pp. 2886-2886 ◽

Cited By ~ 1

Keyword(s):

Holonomic Constraint

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Self-Steering Characteristics of FSAE Vehicle with a Linear Bicycle Model

International Journal of Vehicle Structures and Systems ◽

10.4273/ijvss.13.3.06 ◽

2021 ◽

Vol 13 (3) ◽

Author(s):

Prashanth Barathan ◽

R. Aakash ◽

Hussain Akbar ◽

Kapilesh Kathiresh

Keyword(s):

Critical Speed ◽

Accurate Prediction ◽

Lateral Acceleration ◽

Steering Wheel ◽

Angle Variation ◽

Dynamic Conditions ◽

Operating Range ◽

Bicycle Model ◽

Steering Characteristics

A FSAE car must exhibit precise and predictable handling behaviour since it is subject to driving manoeuvres in dynamic conditions. Therefore, an accurate prediction of its self-steering characteristics becomes vitally important, especially in the expected lateral acceleration operating range. The simulation implements a linear bicycle model of FSAE car in MATLAB and establishes the understeer gradient and the critical speed, thereby aiding the analysis of the steering wheel angle variation required to negotiate the corners of increasing dynamics.

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Residual Attention Network-based Confidence Estimation Algorithm for Non-holonomic Constraint in GNSS/INS Integrated Navigation System

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2021.3113500 ◽

2021 ◽

pp. 1-1

Author(s):

Yimin Xiao ◽

Haiyong Luo ◽

Fang Zhao ◽

Fan Wu ◽

Xile Gao ◽

...

Keyword(s):

Navigation System ◽

Estimation Algorithm ◽

Integrated Navigation ◽

Confidence Estimation ◽

Attention Network ◽

Holonomic Constraint ◽

Integrated Navigation System

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Lateral control of an autonomous vehicle using integrated backstepping and sliding mode controller

Proceedings of the Institution of Mechanical Engineers Part K Journal of Multi-body Dynamics ◽

10.1177/1464419318797051 ◽

2018 ◽

Vol 233 (1) ◽

pp. 141-151 ◽

Cited By ~ 5

Author(s):

Armin Norouzi ◽

Milad Masoumi ◽

Ali Barari ◽

Saina Farrokhpour Sani

Keyword(s):

Optimization Algorithm ◽

Sliding Mode ◽

Autonomous Vehicle ◽

Lane Change ◽

Lateral Control ◽

Lane Changing ◽

Dynamic Constraints ◽

Bicycle Model ◽

Double Lane Change ◽

Backstepping Controller

In this paper, a novel Lyapunov-based robust controller by using meta-heuristic optimization algorithm has been proposed for lateral control of an autonomous vehicle. In the first step, double lane change path has been designed using a fifth-degree polynomial (quantic) function and dynamic constraints. A lane changing path planning method has been used to design the double lane change manoeuvre. In the next step, position and orientation errors have been extracted based on the two-degree-of-freedom vehicle bicycle model. A combination of sliding mode and backstepping controllers has been used to control the steering in this paper. Overall stability of the combined controller has been analytically proved by defining a Lyapunov function and based on Lyapunov stability theorem. The proposed controller includes some constant parameters which have effects on controller performance; therefore, particle swarm optimization algorithm has been used for finding optimum values of these parameters. The comparing result of the proposed controller with backstepping controller illustrated the better performance of the proposed controller, especially in the low road frictions. Simulation of designed controllers has been conducted by linking CarSim software with Matlab/Simulink which provides a nonlinear full vehicle model. The simulation was performed for manoeuvres with different durations and road frictions. The proposed controller has outperformed the backstepping controller, especially in low frictions.

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BDS PPP/INS Tight Coupling Method Based on Non-Holonomic Constraint and Zero Velocity Update

IEEE Access ◽

10.1109/access.2020.3008849 ◽

2020 ◽

Vol 8 ◽

pp. 128866-128876

Author(s):

Wei Sun ◽

Yihan Yang

Keyword(s):

Coupling Method ◽

Tight Coupling ◽

Holonomic Constraint ◽

Zero Velocity

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A Systematic Approach to Identifying a Set of Feasible Designs

Volume 2B: 43rd Design Automation Conference ◽

10.1115/detc2017-68003 ◽

2017 ◽

Author(s):

Hyeongmin Han ◽

Sehyun Chang ◽

Harrison Kim

Keyword(s):

Design Problem ◽

Constraint Satisfaction Problem ◽

Optimization Technique ◽

Iteration Process ◽

Function Evaluation ◽

Design Variables ◽

Bicycle Model ◽

Engineering Design Problems ◽

Data Points ◽

Feasible Solutions

In engineering design problems, designers set boundaries of design variables and solve the system to find the design variables that satisfy a target performance. Once lower and upper bounds for each performance index are set, the design problem becomes Constraint Satisfaction Problem (CSP). In this paper, CSP problem is transformed into an optimization problem with a penalty function. Also, by applying optimization technique, set of feasible solutions are acquired. The set of solutions and all the function evaluation during the iteration process are stored in database. By utilizing a database query, the best solution among the data points are selected for the design problem. For the numerical experiment, a CSP with three variables and a bicycle model of vehicle design is tested with different scenarios.

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