Improving gearshift controllers for electric vehicles with reinforcement learning

Beaudoin, Marc-Antoine; Boulet, Benoît

doi:10.1016/j.mechmachtheory.2021.104654

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…The CAB algorithm is a novel cluster-based algorithm for collaborative recommendation tasks. It implements a potential feedback-sharing mechanism by estimating users' neighborhoods in a context-dependent manner [8]. The CAB algorithm makes a drastic change to the current state of models by incorporating synergistic effects into the reasoning and learning process in a way that seamlessly interweaves exploration-exploitation trade-offs and joins synergistic steps [9].…”

Section: Cab Algorithmmentioning

confidence: 99%

Improvement of the recommendation system based on the multi-armed bandit algorithm

2024

ACE

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In order to effectively solve common problems of the recommendation system, such as the cold start problem and dynamic data modeling problem, the multi-armed bandit (MAB) algorithm, the collaborative filtering (CF) algorithm, and the user information feedback are applied by researchers to update the recommendation model online and in time. In other words, the cold start problem of the recommendation system is transformed into an issue of exploration and utilization. The MAB algorithm is used, user features are introduced as content, and the synergy between users is further considered. In this paper, the author studies the improvement of the recommendation system based on the multi-armed bandit algorithm. The Liner Upper Confidence Bound (LinUCB), Collaborative Filtering Bandits (COFIBA), and Context-Aware clustering of Bandits (CAB) algorithms are analyzed. It is found that the MAB algorithm can get a good maximum total revenue regardless of the content value after going through the cold start stage. In the case of a particularly large amount of content, the CAB algorithm achieves the greatest effect.

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Section: Cab Algorithmmentioning

confidence: 99%

Improvement of the recommendation system based on the multi-armed bandit algorithm

2024

ACE

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“…According to the above equation, the value function L x [0] of the initial state can be solved only by computing the probability distribution p x [t] of the state x [t] for all steps. The Probability distribution for each step is given by (28), as shown at the bottom of the next page.…”

Section: ) Strategy Evaluationmentioning

confidence: 99%

“…. , p x [T ] for all steps can be solved by repeating the above four steps, and the value function L x [0] of the initial state can be calculated by using the resulting state distribution and performing the integration operation according to equation (28).…”

Section: ) Strategy Evaluationmentioning

confidence: 99%

“…This approach enables precise and efficient control optimization with only a few experiments [22], [23]. Previous studies have applied PILCO to different engineering disciplines, including an autopilot underwater vehicle control problem [24], [25], constant force control of a robot surface [26], autonomous optimization of PID parameters in the control of a flight attitude simulator [27], and experiments on a planetary gear transmission shift console frame [28]. These studies have shown that PILCO can obtain the ideal controller strategy for the gearbox with a relatively small number of experiments.…”

Section: Introductionmentioning

confidence: 99%

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The Optimization of RBFNN Gearshift Controller Parameters for Electric Vehicles Using PILCO Reinforcement Learning

Liu,

Zhang,

et al. 2023

IEEE Access

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In order to improve the efficiency of gearshift controller parameter optimization and obtain a good gearshift controller control effect, this article proposes an optimization method for electric vehicle transmission gearshift controller, and selects dual clutch transmission as the research object that establishes an 11-degree-of-freedom gearshift dynamics model and a feedforward-feedback gearshift control model. The feedforward control is chosen from the target trajectory given by the Legendre pseudo-spectral approach, and the feedback controller is a Gaussian kernel radial basis function neural network controller. The feedback controller performs parameter optimization by the Probabilistic Inference for Learning Control (PILCO) reinforcement learning algorithm to obtain a control strategy that matches the actual gearshift conditions. By comparing how well the master-slave moving disk can follow the target trajectory during various optimization iterations, it is verified that the algorithm requires only a few experiments to complete the optimization, and the optimized Radial Basis Function Neural Network (RBFNN) control has a better control effect by comparing the results of different iterations. Applying the learned controller to various slope and load circumstances yields data that demonstrate that all can have an obvious optimization effect with good robustness. Additionally, the reinforcement learning technique suggested in this research can be used for various gearshift controller parameter optimization to assist engineers and technicians in increasing the effectiveness of their Research and Development.INDEX TERMS Electric vehicle, dual clutch transmission, gearshift control, reinforcement learning.

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“…Their further work proposes three planning torque trajectories to control the electric machine torque so as to achieve optimal gear shifting including the vehicle jerk and shift duration [27]. In addition, research [28] uses a model-based reinforcement learning algorithm to calibrate the gearshift controller parameters, which is advantageous for investigating different gearshift control approaches. Nevertheless, there aren't many studies on how to optimize the control of the gearshift actuator, particularly when using linear actuators.…”

Section: Introductionmentioning

confidence: 99%

Precise and coordinated gearshift control for AMT gearshift system equipped with two linear actuators

Lin,

Li,

Tang

et al. 2024

Mechanics & Industry

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A type of automated manual transmission gearshift system that uses two linear actuators to perform gearshift events is presented. The shifting mechanism can be made simpler by using linear actuator to drive the shift fork directly. The mathematical model of the actuator is built and analyzed. Coordinated control of the engagement and disengagement processes is created based on a detailed analysis of the gearshift process in order to shorten the shift time. The force characteristics test reveals the nonlinear output characteristics. The precise displacement control need for coordinated control and stable control requirement for the shifting operation are satisfied by a linear extended state observer based controller. To verify the novel gearshift system and the control method, a test bench and control system are constructed. The results of the simulations indicate that the displacement control's accuracy is enhanced. As the speed difference reaches 500 r/min, the testing results demonstrate a noticeably reduced of shift force fluctuation and jerk generated during the gearshift process, from 20 m/s3 to 3.1 m/s3. With the improved gearshift system and control method, the gearshift coordinated control can also save 25 ms of shift time.

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Improving gearshift controllers for electric vehicles with reinforcement learning

Cited by 7 publications

References 19 publications

Improvement of the recommendation system based on the multi-armed bandit algorithm

Improvement of the recommendation system based on the multi-armed bandit algorithm

The Optimization of RBFNN Gearshift Controller Parameters for Electric Vehicles Using PILCO Reinforcement Learning

Precise and coordinated gearshift control for AMT gearshift system equipped with two linear actuators

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