Yaw stability control for a rear double-driven electric vehicle using LPV-H∞ methods

Weiss, Yonathan; Allerhand, Liron I.; Arogeti, Shai

doi:10.1007/s11432-017-9339-7

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…For future work, an active suspension control system will be developed to improve the riding comfort and vehicle's sprung mass leveling, which will be Proc IMechE Part C: J Mechanical Engineering Science 236 (2) integrated with the developed lateral stability controller to maximize the vehicle performance while driving on rough terrain.…”

Section: Discussionmentioning

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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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: Discussionmentioning

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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“…The linear two-degree-of-freedom model is used as the reference model for calculating the nominal value [11,12]. As shown in Figure 4.…”

Section: Calculation Of Nominal Valuementioning

confidence: 99%

Development and Validation of Electronic Stability Control System Algorithm Based on Tire Force Observation

Yin

et al. 2020

Applied Sciences

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In view of the higher and higher assembly rate of the electronic stability control system (ESC in short), the control accuracy still needs to be improved. In order to make up for the insufficient accuracy of the tire model in the nonlinear area of the tire, in this paper, an algorithm for the electronic stability control system based on the control of tire force feedforward used in conjunction with tire force sensors is proposed. The algorithm takes into consideration the lateral stability of the tire under extreme conditions affected by the braking force. We use linear optimal control to determine the optimal yaw moment, and obtain the brake wheel cylinder pressure through an algorithm combining feedforward compensation based on measured tire force and feedback correction. The controller structure is divided into two layers, the upper layer is controlled by a linear quadratic regulator (LQR in short) and the lower layer is controlled by PID (Proportional-integral-derivative) and feedforward. After that, verification of the controller’s algorithms using software cosimulation and hardware-in-the-loop (HIL in short) testing in the double lane change (DLC in short) and sine with dwell (SWD in short) conditions. From the test results it can be concluded that the controller based on tire force observation has partially control advantages.

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“…It is well known that a gain-scheduled (GS) control is an effective and reliable control technique to deal with systems having uncertain parameters and to evaluate the performance of control efforts. In [10], a new robust stability controller based on the GS H ∞ control for an LPV model of the electric vehicle was investigated against the parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Robust SOF Stackelberg game for stochastic LPV systems

Mukaidani

2021

Sci. China Inf. Sci.

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A robust static output feedback (SOF) Stackelberg game with multiple followers in stochastic linear parameter varying (LPV) systems is investigated. The conditions for the existence of a robust SOF Stackelberg strategy set under H∞ constraints are established by the cross-coupled matrix inequality (CCMI). To determine this strategy set, the optimization problems corresponding to the relevant cost bounds are defined, and their solution sets are derived using the Karush-Kuhn-Tucker conditions. The results show that the robust SOF Stackelberg strategy set can be obtained by solving higher-order cross-coupled matrix equations (CCMEs). Because CCMEs are complex and difficult to solve numerically, a heuristic algorithm is developed by combining the CCMEs with the CCMIs. The convergence property is proven using the Krasnoselskii-Mann (KM) iteration algorithm. Finally, two numerical examples are solved to demonstrate the reliability and usefulness of the proposed heuristic algorithm.

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Yaw stability control for a rear double-driven electric vehicle using LPV-H∞ methods

Cited by 9 publications

References 28 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

Development and Validation of Electronic Stability Control System Algorithm Based on Tire Force Observation

Robust SOF Stackelberg game for stochastic LPV systems

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