Adaptive yaw stability control by coordination of active steering and braking with an optimized lower‐level controller

This paper presents a vehicle stability control method based on a multi-input multi-output (MIMO) model reference adaptive control (MRAC) strategy as an advanced driver assistance system (ADAS) to enhance the handling and yaw stability of the vehicle lateral dynamics. The corrective yaw moment and additive steering angle are generated using direct yaw moment control (DYC) and active front steering (AFS) at the upper control level in the hierarchical control algorithm. A nonlinear term is added to the conventional adaptive control laws to handle parametric uncertainties and disturbances. The desired yaw moment generated by the upper-level controller is converted to the brake forces and is distributed to the rear wheels by an optimal procedure at the lower-level. The major contribution of this study is the introduction of a nonlinear integrated adaptive control method based on a constraint optimization algorithm. To verify the effectiveness of the proposed control strategy, the nonlinear integrated adaptive controller, and linear time-varying MRAC are designed and used for comparison. Simulation results are performed for the J-turn and double lane change (DLC) manoeuvres at high speeds and low tyre-road friction coefficients. The desired performance of the proposed controller exhibited significant improvement compared to the conventional MRAC in terms of yaw rate tracking and handling of sideslip limitation.

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“…The bicycle model is used as the reference model with the following assumption, Mz*=0, μ=1, β=tan⁡−1true(vyvxtrue), Mdz=0, and Fdy=0. 23…”

Section: System Descriptionmentioning

confidence: 99%

“…The second constraint, which indicates the physical limitation as well as the vehicle's lateral stability range for the tyre, is as follows. 23,32 …”

Section: Hierarchical Controller Designmentioning

confidence: 99%

Managing driving disturbances in lateral vehicle dynamics via adaptive integrated chassis control

Ahmadian

Khosravi

Sarhadi

2020

Proceedings of the Institution of Mechanical Engineers, Part K:

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“…However, the resulting control performance is not satisfactory. A neural network-based PI algorithm was proposed in Peng et al, 16 but the hybrid controller still showed a weak improvement compared to the pure PI controller in Wang et al 15 To further improve the control performance, some nonlinear and optimization-based controllers were employed to enhance the DYC control performance, such as linear and nonlinear model predictive control (MPC), 17 robust H ∞ control, 18 adaptive control, 19,20 etc. Zhao et al 21 released a μ controller to track desired sideslip angle and yaw rate upon a four-wheel steer-by-wire vehicle.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control allocation based on adaptive sliding mode control strategy for enhancing the lateral stability of four-wheel-drive electric vehicles

Ao¹,

Wong²,

Huang

2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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A novel hierarchical direct yaw moment controller is designed to enhance the lateral stability of the four-wheel-drive electric vehicle. The adaptive sliding mode control (ASMC) technique in the upper-layer controller is employed to compute an additional yaw moment. The lower-layer controller distributes this yaw moment into each independent wheel by utilizing model predictive control allocation (MPCA). The proposed MPCA aims to mitigate the performance deterioration induced by in-wheel motor dynamics and optimize the power consumption stemming from the additional yaw moment. Co-simulation and hardware-in-the-loop (HIL) test is conducted to verify the performance of the proposed controller. Validation results show that the proposed hierarchical ASMC-MPCA controller outperforms the sliding mode control MPCA (SMC-MPCA) and the integrated nonlinear model predictive control (NMPC) with the lowest root-mean-square errors [Formula: see text] of yaw rate, sideslip angle, lateral deviation, and lowest power consumption. Additionally, the chattering phenomenon in SMC-MPCA can be suppressed effectively by adaptively estimating the parameter uncertainties. The proposed ASMC-MPCA controller also consumes less computational resources than the NMPC and SMC-MPCA, which indicates that the ASMC-MPCA is more suitable for an automotive onboard controller. The comparison between hierarchical and integrated controller frameworks also shows that the hierarchical framework is more suitable for production vehicles under non-powerful vehicle control units.

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“…Integrating and coordinating DYC and AFS is a useful approach to limit the excessive use of external yaw moment while maintaining stability [14][15][16]. To further improve the maneuvering and yaw stability of the vehicle, researchers have employed a variety of control algorithms such as sliding mode control (SMC) [17,18], fuzzy logic control [19], optimal control [20,21], model predictive control [22,23], and so on.…”

Section: Introductionmentioning

confidence: 99%

Optimal Coordinated Control of Active Front Steering and Direct Yaw Moment for Distributed Drive Electric Bus

Lin

Zou

Liang³

et al. 2023

Machines

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This paper suggests a hierarchical coordination control strategy to enhance the stability of distributed drive electric bus. First, an observer based on sliding mode observer (SMO) and adaptive neural fuzzy inference system (ANFIS) was designed to estimate the vehicle state parameters. Then the upper layer of the strategy primarily focuses on coordinating active front steering (AFS) and direct yaw moment control (DYC). The phase plane method is utilized in this layer to provide an assessment basis for the switching control safety of AFS and DYC. The lower layer of the strategy designs an integral terminal sliding mode controller (ITSMC) and a non-singular fast terminal sliding mode controller (NFTSMC) to obtain the optimal additional front wheel steering angle to improve handling performance. A fuzzy sliding mode controller (FSMC) is also proposed to obtain additional yaw moment to ameliorate yaw stability. Finally, the strategy proposed in this paper is subjected to simulation testing and compared with the performance of AFS and DYC systems. The proposed strategy is also evaluated for tracking errors in sideslip angle and yaw rate under two conditions. The results demonstrate that the proposed strategy can effectively adapt to various extreme environments and improve the maneuvering and yaw stability of the bus.

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Adaptive yaw stability control by coordination of active steering and braking with an optimized lower‐level controller

Cited by 11 publications

References 36 publications

Managing driving disturbances in lateral vehicle dynamics via adaptive integrated chassis control

Managing driving disturbances in lateral vehicle dynamics via adaptive integrated chassis control

Model predictive control allocation based on adaptive sliding mode control strategy for enhancing the lateral stability of four-wheel-drive electric vehicles

Optimal Coordinated Control of Active Front Steering and Direct Yaw Moment for Distributed Drive Electric Bus

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