Data-Driven Predictive Gearshift Control for Dual-Clutch Transmissions and FPGA Implementation

Lu, Xiaohui; Chen, Hong; Gao, Bingzhao; Zhang, Zhenwei; Jin, Wen

doi:10.1109/tie.2014.2312312

Cited by 54 publications

(24 citation statements)

References 38 publications

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“…In this case, the EKF algorithm is implemented in software, using a Nios II soft processor running at 100MHz, and a custom hardware accelerator is used for singleprecision floating-point matrix operations. Also in the automotive area, similar problems regarding the efficient implementation of matrix operations are addressed in [74] for the design of a predictive speed controller in dual-clutch transmission gearshifts. Much better performance could be obtained in both cases by using current devices, with more powerful embedded processors and DSP blocks.…”

Section: B Advanced Control Techniquesmentioning

confidence: 99%

Advanced Features and Industrial Applications of FPGAs—A Review

Rodríguez-Andina

Valdes-Pena

Moure

2015

IEEE Trans. Ind. Inf.

147

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research interests include the design of SoC systems based on FPGA, focusing on signal acquisition and conditioning, signal processing, and wireless sensors networks for industrial applications.María J. Moure (M'99) is an Associate Professor at the Department of Electronic Technology, University of Vigo. Her research interests include the design of SoC systems based on FPGA, focusing on signal acquisition and conditioning, signal processing, and wireless sensors networks for industrial applications.

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Section: B Advanced Control Techniquesmentioning

confidence: 99%

Advanced Features and Industrial Applications of FPGAs—A Review

Rodríguez-Andina

Valdes-Pena

Moure

2015

IEEE Trans. Ind. Inf.

147

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“…Walker and Zhang designed a proportional-integral-derivative controller using the speed difference between the differential and the wheel as the input to suppress the residual torque oscillation after the inertia phase of an up shift [18]. Lu et al proposed a data-driven predictive control based gearshift control strategy to track the clutch speed difference reference trajectory by manipulating the oncoming clutch torque and engine output torque during the inertia phase of a clutch-to-clutch shift [19].…”

Section: Et Al Presented a Disturbance Compensation Based Torque Coomentioning

confidence: 99%

“…Taking the two constraints (20) and (21) into consideration and incorporating the control-oriented clutch model (19) and the clutch actuator controller (7) into the simulation-oriented clutch actuator model (3), the entire control-oriented clutch model can be formulated as:…”

Section: Control-oriented Modelmentioning

confidence: 99%

Drivability-Related Discrete-Time Model Predictive Control of Mode Transition in Pre-Transmission Parallel Hybrid Powertrains

Guo

Yan

2016

Energies

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During the mode transition from the pure electric propulsion mode to the hybrid propulsion mode, clutch-based pre-transmission parallel hybrid electric vehicles are subject to drivability issues. These issues originate from the fact that in the clutch-based pre-transmission parallel hybrid powertrain (CPPHP) configuration, the clutch connects the engine and the motor. Without a carefully designed mode transition control that coordinates the engine torque, clutch torque and motor torque, torque sluggishness and surges occur during the mode transition, and residual torque oscillation occurs after the mode transition. In this paper, a discrete-time model predictive control (DMPC)-based controller is proposed to address these drivability-related issues. Modeling improvements and novel drivability-related indices and constraints are all taken into consideration in the design of the discrete-time model predictive controller. Furthermore, by using discrete-time Laguerre functions and introducing the equilibrium state and the ranking of constraints, an explicit solution of the discrete-time model predictive controller is obtained. The calculation results demonstrate that the proposed controller can ensure a smooth and rapidly decaying torque difference during the mode transition, alleviating the residual torque oscillation after the mode transition and guaranteeing that the mode transition is completed within an acceptable duration.

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“…DDPC has been used for a multidimensional blast furnace system [18], wind power generation [19], the distributed solar collector field [20], biped robots [21], a waste water system [22], a vapor compression refrigeration cycle system [23] and other industrial applications. Furthermore, a small quantity of application research works relative to DDPC have been used for fast and dynamic situations, such as automated manual transmission (AMT) vehicle starting [24], dual-clutch transmission [25] and high-speed train operation [26]. Since the actual engine torque, the clutch torque and the motor torque and their change rates are all limited by actuator saturation, the torque coordination control of HEV powertrain is a multiple-constraint optimal control problem.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Predictive Torque Coordination Control during Mode Transition Process of Hybrid Electric Vehicles

2017

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Torque coordination control significantly affects the mode transition quality during the mode transition dynamic process of hybrid electric vehicles (HEV). Most of the existing torque coordination control methods are based on the mechanism model, whose control effect heavily depends on the modeling accuracy of the HEV powertrain. However, the powertrain structure is so complex, that it is difficult to establish its precise mechanism model. In this paper, a torque coordination control strategy using the data-driven predictive control (DDPC) technique is proposed to overcome the shortcomings of mechanism model-based control methods for a clutch-enabled HEV. The proposed control strategy is only based on the measured input-output data in the HEV powertrain, and no mechanism model is needed. The conflicting control requirements of comfortability and economy are included in the cost function. The actual physical constraints of actuators are also explicitly taken into account in the solving process of the data-driven predictive controller. The co-simulation results in Cruise and Simulink validate the effectiveness of the proposed control strategy and demonstrate that the DDPC method can achieve less vehicle jerk, faster mode transition and smaller clutch frictional losses compared with the traditional model predictive control (MPC) method.

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Data-Driven Predictive Gearshift Control for Dual-Clutch Transmissions and FPGA Implementation

Cited by 54 publications

References 38 publications

Advanced Features and Industrial Applications of FPGAs—A Review

Advanced Features and Industrial Applications of FPGAs—A Review

Drivability-Related Discrete-Time Model Predictive Control of Mode Transition in Pre-Transmission Parallel Hybrid Powertrains

Data-Driven Predictive Torque Coordination Control during Mode Transition Process of Hybrid Electric Vehicles

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