Ride and Handling Analysis for an Active Anti-Roll Bar: Case Study on Composite Nonlinear Control Strategy

Zamzuri, Hairi; Mazlan, Saiful Amri; Zulkarnain, Noraishikin

doi:10.15282/ijame.10.2014.28.0179

Cited by 21 publications

(21 citation statements)

References 25 publications

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“…The vehicle stability may be affected by some expected and unexpected factors including the vehicle's structure and parameters, the initial operation of the vehicle, tire steer-3477 ing angle, road conditions, side wind force, tire pressure loss or vehicle driving situations [1].Under these almost unpredictable conditions, the driver may not respond within a very short time; therefore, it is necessary to develop secure automatic control approaches. There are several methods to control the yaw rate of a vehicle such as direct yaw moment control [2], four wheel steering system control [3], active differential braking [4], semi active steering system [5,6], and active front steering (AFS) control [6].…”

Section: Introductionmentioning

confidence: 99%

Integrated bees algorithm and artificial neural network to propose an efficient controller for active front steering control of vehicles

Aalizadeh¹,

Asnafi²

2022

Int. J. Automot. Mech. Eng.

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In this paper, a new optimal adaptive controller for the active front steering control of a vehicle is proposed. Due to the availability and applicability of proportional-integrator-derivative (PID) controllers, this controller is picked up; but, to overcome its limitations, two optimization and adaptation schemes are employed. The reference transfer function between the yaw rate of a typical vehicle and its steering angle is derived. The actual dynamics is simulated using CarSim toolbox of MATLAB. Best vehicle handling was aimed to be reached for three famous driving manoeuvres by proposing an efficient but economic controller. An optimization is done on the PID coefficients for a specific road condition using the honey bees' algorithm, and then a two-layer artificial neural network is trained using the back propagation learning rule to adjust the controller coefficients for any arbitrary road conditions. The uncontrolled, desired, optimized controlled, and optimized plus trained controlled yaw rate of vehicles are drawn for three manoeuvers and three road conditions. The integral of squared error between the desired and actual trajectories for different manoeuvers and road conditions are evaluated and compared between each other. The performance of the proposed PID controller that was optimized by Bees Algorithm and trained by a neural network was proved. It is noted that the optimized PID controller is good for all road conditions but not excellent. The more we move away from the reference road (in which, the optimization is done), the error will be larger. Thus for other conditions, using the artificial neural network can help decrease the error significantly.

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Section: Introductionmentioning

confidence: 99%

Integrated bees algorithm and artificial neural network to propose an efficient controller for active front steering control of vehicles

Aalizadeh¹,

Asnafi²

2022

Int. J. Automot. Mech. Eng.

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“…Steering operability contributes greatly to the comfort of the driver when driving a car and with a comfortable steering operation [1,2]; it is possible to reduce shoulder stiffness and arm fatigue caused by long-term driving [3]. Various types of assistances devices were designed to assist lateral control.…”

Section: Introductionmentioning

confidence: 99%

Improvement in Steering Performance by Push-Pull Operation in Car Driving

Kajiwara¹

2018

INT. J. AUTOMOT. MECH. ENG.

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Collision avoidance is one of the most difficult driving operations in the domain of intelligent vehicles. Steering operation error is one of the causes of traffic accidents. Steering operability also contributes greatly to the comfort of drivers and their mental workload during driving. Thus, with comfortable steering operation it is possible to reduce shoulder stiffness and arm fatigue caused by long-time driving. The development of automatic driving vehicles is becoming advanced worldwide. Consequently, the goal is to implement a level 3 automatic driving vehicle for practical use, in which "the system performs the acceleration, the steering, and the braking, and the driver responds when the system requests it". For this system to be practical, when autonomous driving becomes impossible for some reason, it is important to develop a system that allows the driver to be in control regardless of driving experience or age. In conventional round steering wheel, since there is no output muscle directly involved in the direction of movement of the upper limbs, it is difficult to instantaneously exert a large force and control performance is not good. Therefore, a twin lever steering (TLS), which is a push / pull alternate steering system, is proposed in this study. In the proposed TLS, there are output muscles that are directly involved in the direction of movement of the upper limb, including the biceps brachii muscle, and it is conceivable that the motor controllability is higher than in the conventional system. In this research, the superiority of the proposed TLS was verified using an experimental racing kart and driving simulator (DS). Furthermore, using DS, the superiority of a TLS in the steering operation immediately after the cancellation of automatic operation is clear.

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“…However, a lighter chassis structure will cause the structural resonance within the typical rigid body vibration to easily occur due to induced dynamic forces by road irregularities, engine, and other loads. This situation can result in riding discomfort and problems in ride safety and stability [5][6][7][8]. Computer based analysis techniques such as the finite element method proves to be a reliable tool in engineering design and product development [9].…”

Section: Introductionmentioning

confidence: 99%

Dynamics properties of a Go-kart chassis structure and its prediction improvement using model updating approach

Abdullah

Sani

Husain

et al. 2017

Int. J. Automot. Mech. Eng.

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Model updating is concerned about the correction of finite element models by processing the record of dynamic response from test structures in order to have an accurate model for any simulated analysis. Finite element model updating had emerged years ago as an important subject in structural dynamics. It has been used frequently and has been successfully applied to many fields especially in detecting the dynamic stiffness of a structure. The purpose of this study is to perform model updating of a go-kart chassis structure in order to reduce the percentage of error between the experimental modal analysis (EMA) and finite element analysis (FEA). Modal properties (natural frequency, mode shapes, and damping ratio) of the go-kart chassis structure were determined using both EMA and FEA. Correlation of the modal parameters gathered in FEA and EMA was carried out before optimizing the data from finite element. By adjusting the selective parameters, incongruities between those two analyses are generally reduced. The sensitivity of selected parameters is also obtained. The significant reduction in percentage of error before and after model updating procedure was carried out in this study clearly shows that model updating technique is a reliable method in reducing the discrepancies between EMA and FEA. Therefore, in cases of high discrepancies between analytical and actual test data, model updating can be considered as an option in order to obtain better correlation between those two sets of data.

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Ride and Handling Analysis for an Active Anti-Roll Bar: Case Study on Composite Nonlinear Control Strategy

Cited by 21 publications

References 25 publications

Integrated bees algorithm and artificial neural network to propose an efficient controller for active front steering control of vehicles

Integrated bees algorithm and artificial neural network to propose an efficient controller for active front steering control of vehicles

Improvement in Steering Performance by Push-Pull Operation in Car Driving

Dynamics properties of a Go-kart chassis structure and its prediction improvement using model updating approach

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