Automated calibration of gearshift controllers using iterative learning control for hybrid systems

Mishra, Kirti D.; Cardwell, Guy; Srinivasan, K.

doi:10.1016/j.conengprac.2021.104786

Cited by 11 publications

(9 citation statements)

References 25 publications

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“…None of the methods reported above learn the feedback controller from iterated trials with a transmission test bench. The closest to it would be the work reported in [2], where researchers use iterative learning control (ILC) to tune the parametrization of a feedforward signal for the closure of Clutch 2. The experimental results show that very few trials are required to learn appropriate parameters.…”

Section: The Clutch-to-clutch Gearshift Control Problemmentioning

confidence: 99%

“…At this point, engineers typically rely solely on statistics to infer the best combination of parameters from the recorded gearshift trials -an approach centered around the design of experiments (DOE) [1]. This approach can be time and resource consuming, sometimes requiring thousands of gearshift trials [2,3,4]. This is because despite modern advances [5], DOE-like methods treat the mapping from the controller parameters to the gearshift performance indicator as a black box.…”

Section: Introductionmentioning

confidence: 99%

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Improving gearshift controllers for electric vehicles with reinforcement learning

Beaudoin,

Boulet

2021

Preprint

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During a multi-speed transmission development process, the final calibration of the gearshift controller parameters is usually performed on a physical test bench. Engineers typically treat the mapping from the controller parameters to the gearshift quality as a black-box, and use methods rooted in experimental design -a purely statistical approach -to infer the parameter combination that will maximize a chosen gearshift performance indicator. This approach unfortunately requires thousands of gearshift trials, ultimately discouraging the exploration of different control strategies. In this work, we calibrate the feedforward and feedback parameters of a gearshift controller using a model-based reinforcement learning algorithm adapted from pilco. Experimental results show that the method optimizes the controller parameters with few gearshift trials. This approach can accelerate the exploration of gearshift control strategies, which is especially important for the emerging technology of multi-speed transmissions for electric vehicles.

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Section: The Clutch-to-clutch Gearshift Control Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving gearshift controllers for electric vehicles with reinforcement learning

Beaudoin,

Boulet

2021

Preprint

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“…Iterative learning control (ILC) is an effective control methodology that can enhance the tracking accuracy of dynamical systems by learning from previous iterations data [1,2], and has been applied to many applications like biomedical engineering [3,4], batch processes [5][6][7], robotic systems [8,9], urban traffic control [10,11], and so forth [12][13][14][15][16]. A more detailed discussion about various ILC designs in the continuous-or discrete-time domain can be found in [1,17].…”

Section: Introductionmentioning

confidence: 99%

Robust convergence conditions of iterative learning control for time‐delay systems under random non‐repetitive uncertainties

Shokri-Ghaleh

Ganjefar

Shahri

2022

IET Control Theory & Appl

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Some of the most fundamental assumptions in the design of iterative learning control (ILC) for uncertain systems are the strict repetitiveness (i.e. iteration-independence) of uncertainties in all factors of plant dynamic, external disturbances, initial conditions, and reference trajectory, which may not always hold in practical applications. The simultaneous relaxation of all these assumptions is a challenging problem that has been addressed only for delay-free systems. This problem is still open for time-delay systems, especially when the time-delay factor also has non-repetitive (i.e. iteration-dependent/varying) uncertainty. Hence, this study extends the problem of robust ILC for a class of time-delay systems under nonrepetitive uncertainties in not only plant dynamic, external disturbances, initial conditions, and reference trajectory but also time-delay. By using the ILC scheme introduced in this work, and based on the frequency domain analysis, it is shown that both monotonic convergence and boundedness of the expected tracking error can be achieved (in the sense of L 2 -norm) when the non-repetitiveness (i.e. iteration-dependence) of all existing uncertainties from a random viewpoint are taken into account. The effectiveness of the proposed strategy is verified by two simulation examples.

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“…sb designed the optimal gearshift control strategy which includes a PID controller or robust two degree-of-freedom (dof) controller based on the dynamics model and gearshift objectives [15]- [17]. Then Mishra presented a model-based automated calibration algorithm and applied it as core Iterative Learning Control (ILC) for gearshift process [18]. Zhao proposed a gearshift control architecture combined with multi-objective trajectory planning by taking gearshift duration jerk and friction work as optimization objectives [19].…”

Section: Introductionmentioning

confidence: 99%

Gearshift Control in Engagement Process of Dual-Motor Coaxial Propulsion System for Electric Bus

Lin

et al. 2022

IEEE Access

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Considering single-motor drive system cannot meet the requirements of high-performance electric buses for Beijing Winter Olympics, a dual-motor coaxial propulsion system based on single planet gear set is developed. At the same time, a shift control method based on hierarchical linear quadratic regulator (HLQR) algorithm is designed to improve the poor shift quality caused by the rapid change of output shaft speed in the shift process. In this method, the upper controller adopts robust tracking linear quadratic regulator algorithm to control the speed change rate of the output shaft in the process of gear shifting, so as to reduce the load of gear shifting actuator in the process of gear shifting; the lower controller adopts disturbance suppression linear quadratic regulator algorithm to control the shifting force and improve the adaptability of the system to disturbance conditions Through simulation analysis, the performances of hierarchical linear quadratic regulator algorithm, optimal control algorithm based on variational method and PI algorithm in the process of gear shifting were comparatively analyzed. Simulation results showed that the hierarchical linear quadratic regulator algorithm exhibited a better performance in gear control than the other two algorithms. In addition, a large number of bench test results also proved that compared with PI algorithm, hierarchical linear quadratic regulator algorithm reduced vehicle impact by 19.17% and reduced the shift force by 32.48% in the sacrifice of shift time by 5.03%.

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Automated calibration of gearshift controllers using iterative learning control for hybrid systems

Cited by 11 publications

References 25 publications

Improving gearshift controllers for electric vehicles with reinforcement learning

Improving gearshift controllers for electric vehicles with reinforcement learning

Robust convergence conditions of iterative learning control for time‐delay systems under random non‐repetitive uncertainties

Gearshift Control in Engagement Process of Dual-Motor Coaxial Propulsion System for Electric Bus

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