Steering control of six-wheeled vehicles using linear quadratic regulator techniques

Chen, B.-C.; Yu, Cheng; Hsu, Wei-Tai

doi:10.1243/09544070jauto471

Cited by 10 publications

(7 citation statements)

References 3 publications

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“…34 Hence, the weights matrices Q and R are continuously optimized for each design parameter variation to ensure asymptotic stability with real negative eigenvalues of the closed-loop control system. The weights matrices Q and R are presented according to Bryson's rule [35][36][37] and optimized based on a free constant positive number C Q , the maximum allowable average steering angle of the 3rd and 4th (d 3max: and d 4max: ) as in equations (16) and (17), consequently. For that purpose, a new cost function is synthesized and introduce as in equation (18).…”

Section: Optimal Gslqrmentioning

confidence: 99%

“…Huh et al 15 compared different steering configurations of a 6-wheel vehicle. The study showed that the handling characteristics of the vehicle are improved in case of 6-Wheel Steering (6 WS) than other configurations which were improved later by Chen et al 16 using Linear Quadratic Regulator (LQR) controller. Later, an optimal 6 WS controller was introduced by Kim, Yi, and Lee 17 and Kim et al 18 by constraining the lateral force to the friction circle.…”

Section: Introductionmentioning

confidence: 99%

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A novel adaptive-rear axles steering controller for an 8 × 8 combat vehicle

Ahmed

El–Gindy

Lang

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Multi-axle vehicles are widely used in several applications such as transportation, industrial, and military field, because of its higher reliability in comparison with conventional two axles vehicles. Despite that, there is a paucity of research studies that consider lateral stability enhancement of these vehicles, especially on rough terrain. This simulation-based research study fills this gap and introduces a new adaptive Active Rear Steering (ARS) controller that improves the lateral stability of an 8x8 combat vehicle for rough-terrain operation. The developed controller is designed utilizing the Integral Sliding Mode Control theory (ISMC) based on Gain-Scheduled Linear Quadratic Regulator (GSLQR). Besides, the GSLQR control gains are optimized by a Genetic Algorithm (GA) toolbox using a new synthesized cost function to ensure asymptotic stability. Furthermore, a new Adaptive-ISMC (AISMC) is introduced by using genetic programming to generate control equations that can replace the developed high-dimension GSLQR gains and facilitate future hardware implementation. The developed controller is evaluated by performing a series of simulation-based Double Lane Change (DLC) maneuvers on several rough terrains. The evaluation is conducted for both high friction and slippery surfaces at high and moderate speed, consequently. The results show high fidelity and robustness of the developed controller in comparison with a previously designed optimal LQR controller.

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Section: Optimal Gslqrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel adaptive-rear axles steering controller for an 8 × 8 combat vehicle

Ahmed

El–Gindy

Lang

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…Meanwhile, ARS seems to be more efficient for military combat vehicles as it works in a harsh environment, so noise will not be an issue, and it requires a trained driver to operate it. Despite that, limited studies considered ARS to improve lateral stability of combat vehicles as in [15][16][17] and in 18,19 for a 6x6 and 8 × 8 combat vehicles, respectively.…”

Section: Introductionmentioning

confidence: 99%

Path-following enhancement of an 8×8 combat vehicle using active rear axles steering strategies

Ahmed

El–Gindy

Lang

2021

Proceedings of the Institution of Mechanical Engineers, Part K:

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Active rear steering has been used in many research work to enhance ground vehicles’ lateral stability. However, there is a shortage in the published research studies that consider the incorporation of active rear steering for autonomous vehicles applications, especially in case of multi-axle combat vehicles. In this paper, various H∞ controllers are developed to actively steer rear axles of a multi-axle combat vehicle using a linearized bicycle model. The proposed controllers are incorporated with a 22 degrees of Freedom nonlinear Trucksim full vehicle model to study and compare the developed controllers’ performance on a hard surface. Moreover, a frequency-domain analysis is conducted to investigate the influence of the active rear steering on the path-following controllers’ robustness in terms of stability and performance. Three path-following controllers are designed, where the first controller is applied on the front two axles of the vehicle, while the rear two axles are fixed. The second is applied to all-wheel steering vehicle. The third controller is an integration between the designed front steering path-following controller and a developed lateral stability active rear steering controller. Eventually, a series of virtual maneuvers are performed to evaluate the effectiveness of the intended controllers to present the advantages and limitations of each controller at different driving conditions.

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“…For example, in the early days of dynamical investigation, various scholars extended the vehicle-handling concepts and traditional conventions commonly found in literature relevant to two-axle vehicles, to the three-axle models. Then, some of them developed steering control strategies or controllers based on these three-axle models, such as the linear quadratic regulator technique with integral control (Chen et al, 2007), desired yaw rate (An et al, 2008), and the model-following variable structure (Qu et al, 2008), were analogous to the techniques of the two-axle vehicle. To further improve vehicle stability and manoeuvrability, Shen et al (2016) developed a direct yaw moment control method based on an 8-axle vehicle with 16-independent driving wheels.…”

Section: Introductionmentioning

confidence: 99%

Matching and optimising analysis of multi-axle steering vehicle steering system

Wang

2018

IJVD

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The analysis of the multi-axle steering link mechanism (MASLM) link forces provides the foundation for the mechanism's design. The matching level between the steering cylinder driving torque and the tyre pivot steering resistance torque can dramatically influence the link forces. In this study, the accuracy of the tyre resistance torque formula reached 90.7%. A steady kinematic-mechanical coupling model of the MASLM was built modularly, and a method of calculating the link forces and steering hydraulic pressure is proposed. To verify the coupling models, an all-terrain QAY130 crane was chosen for simulation and testing. Two vehicle models were built by using the Adams and Matlab software, respectively. The discrepancies were less than 23.5% between the test values and the predicted values for the previous two models, but exhibited similar variation trends. The installation site and diameter parameters of the steering cylinders were optimised to minimise the link forces.

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Steering control of six-wheeled vehicles using linear quadratic regulator techniques

Cited by 10 publications

References 3 publications

A novel adaptive-rear axles steering controller for an 8 × 8 combat vehicle

A novel adaptive-rear axles steering controller for an 8 × 8 combat vehicle

Path-following enhancement of an 8×8 combat vehicle using active rear axles steering strategies

Matching and optimising analysis of multi-axle steering vehicle steering system

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