2019
DOI: 10.1109/tcst.2018.2856191
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Embedded Nonlinear Model Predictive Control of Dual-Clutch Transmissions With Multiple Groups on a Shrinking Horizon

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Cited by 15 publications
(3 citation statements)
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“…Similarly, researchers in [15] reported a complete gearshift solution which includes a multi-variable feedback controller designed with the robust H ∞ method. Finally, model predictive control was also used to solve the clutch-to-clutch gearshift problem [16]. In this work, the linear feedback controller used is initially tuned with the linear quadratic regulator (LQR) method, which is obtained from a nominal model of the linear system dynamics.…”
Section: The Clutch-to-clutch Gearshift Control Problemmentioning
confidence: 99%
“…Similarly, researchers in [15] reported a complete gearshift solution which includes a multi-variable feedback controller designed with the robust H ∞ method. Finally, model predictive control was also used to solve the clutch-to-clutch gearshift problem [16]. In this work, the linear feedback controller used is initially tuned with the linear quadratic regulator (LQR) method, which is obtained from a nominal model of the linear system dynamics.…”
Section: The Clutch-to-clutch Gearshift Control Problemmentioning
confidence: 99%
“…the neat integration of MPC on embedded hardware with limited ressources. This might be field programmable gate arrays (FPGA) [44,38,29], programmable logic controllers (PLC) in standard automation systems [42,37] or electronic control units (ECU) in automotive applications [46]. For embedded MPC, several challenges arise in addition to the real-time demand.…”
Section: Introductionmentioning
confidence: 99%
“…The new algorithm is based on an augmented Lagrangian formulation in connection with a real-time gradient method and tailored line search and multiplier update strategies that are optimized for a time and memory efficient implementation on embedded hardware. The performance and effectiveness of augmented Lagrangian methods for embedded nonlinear MPC was recently demonstrated for various application examples on rapid prototyping and ECU hardware level [28,46,16]. Beside the presentation of the augmented Lagrangian algorithm and the general usage of GRAMPC, the paper compares its performance to the nonlinear MPC toolkits ACADO and VIATOC for different benchmark problems.…”
Section: Introductionmentioning
confidence: 99%