Control Strategy for an Electromechanical Transmission Vehicle Based on a Double Markov Process

Liu, Hui; Xu, Riming; Han, Liang; Xiong, Shanshan

doi:10.1007/s12239-021-0069-4

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Inspired from the automotive sector, where a rather limited number of successful implementations of policy iteration algorithms have been published (see, e.g. [21,22]), this article builds upon the seminal work [23]. Our article is similar in the sense that we shall reuse the same idea of implementing policy iteration to compute global optimal policies in conjunction with stochastic models.…”

Section: Our Contributionmentioning

confidence: 99%

“…However, our contribution differs with respect to the following points: (i) the application is different (a power wheelchair, not a passenger car); (ii) the optimization is carried out in terms of total time to complete the task (obstacle avoidance), not energy (e.g. minimize fuel consumption versus battery usage in hybrid electric vehicles [23,24,22,16]); (iii) the framework is different (control of semi-autonomous vehicles, not autonomous vehicles); (iv) the stochastic model is different (user intention, not the driving cycle); (v) environment awareness is ensured online via time-of-flight sensors able to detect obstacles, whereas other means are necessary to identify (classify) driving cycles.…”

Section: Our Contributionmentioning

confidence: 99%

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AssistMe: Policy iteration for the longitudinal control of a non-holonomic vehicle

Teodorescu¹,

Carlson²

2022

Preprint

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In this article we design a physically-inspired model-based assist-as-needed semi-autonomous control (ASC) algorithm to address the problem of safely driving a vehicle (a power wheelchair) in an environment with static obstacles. Once implemented online, the proposed algorithm requires limited computing power and relies on pre-computed (offline) maps (look-up tables). These are readily available by implementing policy iteration that minimizes the expected time to termination (safely stopping near an obstacle), by taking into account: (i) the vehicle dynamics; (ii) the drivers' intention modeled as three separate stochastic processes. We call them the expert driver, the naughty child and the blind driver models.A study with healthy participants confirmed that ASC outperforms a baseline rule-based control (a statistically significant result).

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Section: Our Contributionmentioning

confidence: 99%

Section: Our Contributionmentioning

confidence: 99%

AssistMe: Policy iteration for the longitudinal control of a non-holonomic vehicle

Teodorescu¹,

Carlson²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Inspired from the automotive sector, where a rather limited number of successful implementations of policy iteration algorithms have been published (see, e.g. [19], [20]), this article builds upon the seminal work [21]. Our article is similar in the sense that we shall reuse the same idea of implementing policy iteration to compute global optimal policies in conjunction with a stochastic model.…”

Section: G Our Contributionmentioning

confidence: 99%

AssistMe: Using policy iteration to improve shared control of a non-holonomic vehicle

Teodorescu

Carlson

2022

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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An assist-as-needed semi-autonomous control algorithm is designed to address the problem of safely driving a vehicle (a power wheelchair) in an environment with static obstacles. The main idea is to maximize the human driver's control experience while allowing them to navigate safely (the inputs do not lead to collisions). The proposed physicallyinspired model-based obstacle avoidance algorithm relies on optimal maps of the expected time for executing a safe stop manoeuvre. These maps are pre-computed using policy iteration in the case of an experienced driver stochastic model. As the burden of complex calculations is handled offline, the online implementation of the algorithm requires little computing resources. Its efficiency is tested experimentally in a study with healthy participants: a statistically significant result confirmed that the proposed algorithm outperforms a baseline rule-based control. A discussion with pros and cons ends this paper.

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Research on Fault Technology of Electromechanical System Based on EMD Algorithm

Fan

Liu

2023

Lecture Notes in Electrical Engineering

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Control Strategy for an Electromechanical Transmission Vehicle Based on a Double Markov Process

Cited by 5 publications

References 15 publications

AssistMe: Policy iteration for the longitudinal control of a non-holonomic vehicle

AssistMe: Policy iteration for the longitudinal control of a non-holonomic vehicle

AssistMe: Using policy iteration to improve shared control of a non-holonomic vehicle

Research on Fault Technology of Electromechanical System Based on EMD Algorithm

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