Starting shift control of heavy-duty automatic transmissions based on the optimal trajectory of turbine speed

Li, Chunfu; Meng, Fei; Jiang, Xi; Zhai, Yikui

doi:10.1016/j.ymssp.2019.02.030

Cited by 15 publications

(8 citation statements)

References 21 publications

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“…The TSMC 38 and the PID are comparative controllers, designed as follows For a clear comparison, the simulation case is also applied to these three controllers, and the ADSM controller correspondingly controls the motor. A study on the control parameter selection of the TSM controller can be found in Zhang et al 38 and Li et al, 40 respectively. These control parameters are also obtained by carefully choosing the group with the best tracking performance among many simulation results.…”

Section: Simulation Results Of Engine Speed Trackingmentioning

confidence: 99%

Robust adaptive control for hybrid electric vehicles using sliding mode control

Ding,

Jiao,

Zhang

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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The complicated transient coupling driving performance of the electromechanical coupling driving system for hybrid electric vehicles can be guaranteed by reasonably distributing the power between the engine and the electric machine. However, the disturbances resulting from system parameter perturbation, model uncertainty, unknown road condition, and uncertain preceding vehicle acceleration would degrade the control performance. To this issue, this article investigates a robust adaptive control scheme based on sliding mode control techniques to handle the complicated transient coupling driving problem for the electromechanical coupling driving process. In the robust adaptive control method, the coordinated control mechanism is designed using the electric machine to compensate for the response deviation of engine torque. Meanwhile, the double-loop adaptive terminal sliding mode speed-tracking control scheme and the adaptive dynamic sliding mode torque tracking control method are designed for the engine and the electric machine, respectively. The proposed control scheme ensures accurate tracking of the desired trajectories of the engine and electric machine in the presence of parameter perturbation and the unknown driving condition. Both simulation and actual vehicle experimental results demonstrate the effectiveness and practicality of the proposed control strategy.

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Section: Simulation Results Of Engine Speed Trackingmentioning

confidence: 99%

Robust adaptive control for hybrid electric vehicles using sliding mode control

Ding,

Jiao,

Zhang

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…This subsection will focus on verifying the effectiveness and superiority of the proposed AFTC scheme of the automatic clutch by comparing it with the PID and SMC strategy. Due to adopting the AFTC scheme to handle the influence of parameter uncertainties and external disturbances and gear backlash during the automatic clutch position racking process in this paper, the simulation comparison results between the proposed AFTC scheme and SMC strategy 26 and the PID strategy 40 will be first given. Therefore, the PID strategy and SMC strategy can be designed as follows.…”

Section: Simulation Results Of Automatic Clutch Controllermentioning

confidence: 99%

“…The six parameters are obtained by choosing the group of the best system tracking performance among many simulation results. A study on the control parameter selection of PID and SMC can be found in Hongwei et al 26 and Li, 40 respectively. It is worth noting that the control performances of automatic clutch must be more or less detrimentally affected by the parameter uncertainties and external disturbances or gear backlash.…”

Section: Simulation Results Of Automatic Clutch Controllermentioning

confidence: 99%

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Efficient hierarchical robust adaptive control of mode transition for parallel hybrid electric vehicles

Ding

Jiao

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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A fast and smooth mode transition process for parallel hybrid electric vehicles (HEVs) can improve vehicle drivability. At the same time, system uncertainty resulting from friction, gearbox, parameter perturbation, production deviations, and external disturbance of the mode transition process makes control design challenges. In this regard, this paper introduces a novel efficient hierarchical robust adaptive mode transition control strategy to achieve an accurate and fast engagement of the clutch of a parallel HEV during mode transition in the case of uncertain system parameters and unmeasurable actual clutch torque in this paper. At the upper level of the controller, a robust adaptive sliding mode control (RASMC) is proposed to calculate the clutch transmitted required torque based on the current states of the electric motor (EM) and engine, and the control parameters are optimized via the particle swarm optimization (PSO) algorithm by compromising the vehicle jerk and the clutch slipping energy loss. Moreover, a fuzzy controller is designed to calculate the expected clutch engaging speed by employing the deviation of the desired and actual clutch torques, and the actual clutch torque is not a measured value but an estimate obtained by a proportional-integral (PI) observer. At the lower level, an adaptive finite-time control (AFTC) scheme is developed to overcome the gear backlash, parameter perturbation, and external disturbance during the clutch position tracking process. The effectiveness and advantage of the proposed control strategy are verified by both MATLAB/Simulink simulations and the hardware-in-loop (HIL) test.

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“…There have been many studies on the starting control for DCT vehicles, mainly incorporating physical-model-based optimal control [5]- [7] and intelligent control (particularly, fuzzy control) [8], [9]. For instance, in Ref.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Control Approach for Dual Clutch Transmission Vehicles Starting Process Based on State-Dependent Autoregressive With Exogenous Model

Yang

Wang

Fugen³

et al. 2020

IEEE Access

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To solve the difficulties of modeling the starting system of dual clutch transmission (DCT) vehicles, a state-dependent autoregressive with exogenous variables (SD-ARX) model whose functional coefficients are approximated by sets of radial basis function (RBF) networks is proposed to describe the dynamic characteristics of DCT vehicles starting process in this study. The validity of this modelling approach is verified via a real vehicle test. On this basis, a nonlinear predictive controller based on SD-ARX model is designed. In addition, the physical constraints of this system, including control variables (change rate of engine torque and clutch torque) and state variables (engine speed and clutch speed), are also taken into account during the controller design process via limiting the relevant parameters in particle swarm optimization or setting saturation demand in control program. To verify the validity and merits of the proposed control approach, many sets of simulation analysis in different driving intentions are conducted. Simulation results shown that: the proposed control approach can well control the starting process of DCT vehicles and effectively reflect the demand of driver's intention; compared with the conventional control method, SD-ARX-MPC can improve the starting performance; the proposed approach is robust to a certain extent according to simulation results under changed starting conditions. INDEX TERMS Starting control, radial basis function (RBF) networks, data-driven, state-dependent autoregressive with exogenous (SD-ARX) model, predictive control, nonlinear system.

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Starting shift control of heavy-duty automatic transmissions based on the optimal trajectory of turbine speed

Cited by 15 publications

References 21 publications

Robust adaptive control for hybrid electric vehicles using sliding mode control

Robust adaptive control for hybrid electric vehicles using sliding mode control

Efficient hierarchical robust adaptive control of mode transition for parallel hybrid electric vehicles

Modeling and Control Approach for Dual Clutch Transmission Vehicles Starting Process Based on State-Dependent Autoregressive With Exogenous Model

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