Mining unmanned electric locomotive speed control method based on improved epsilon-greedy strategy and reinforcement learning
The invention discloses a mining unmanned electric locomotive speed control method based on an improved epsilon-greedy strategy and reinforcement learning, and the method comprises the steps: enabling a mining unmanned electric locomotive to serve as an intelligent agent, and setting the motion of t...
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Patent |
| Sprache: | chi ; eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | LI XIAOQIANG ZHU ZHENCAI YAN WANZI LI YING ZHAO ZIYUAN ZHANG YIDONG YE JIMING |
| description | The invention discloses a mining unmanned electric locomotive speed control method based on an improved epsilon-greedy strategy and reinforcement learning, and the method comprises the steps: enabling a mining unmanned electric locomotive to serve as an intelligent agent, and setting the motion of the intelligent agent as an integer within the range of the optimal driving/braking torque applied to an axle by the electric locomotive; the method comprises the following steps: respectively designing actions, states and reward functions of an intelligent agent, and completing the construction of a main body algorithm structure for controlling the speed of the mining unmanned electric locomotive by taking real-time state information of the electric locomotive as input and taking driving/braking torque applied to an axle and a reward value obtained by the intelligent agent as output by utilizing a Q-Learning algorithm. The mining electric locomotive is ensured to autonomously complete behaviors such as safe car fol |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN116424341A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN116424341A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN116424341A3</originalsourceid><addsrcrecordid>eNqNjTEKwkAURNNYiHqH7wFSxAR7CYqNVvZh3YxxYff_ZfcbSOHdjeABrAbevGGWxfvi2PFALw6GGT3Bw2pylrxYCaJuBOWIubHCmsRTgD6lp7vJMxQmF2KS8TuN2XnhckizPlHWZBTDRIZ7SnD8kGQRwEoeJn1v18XiYXzG5perYns63tpziSgdcjQWDO3aa1Xtm11TN9Wh_sf5AOf0ST8</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Mining unmanned electric locomotive speed control method based on improved epsilon-greedy strategy and reinforcement learning</title><source>esp@cenet</source><creator>LI XIAOQIANG ; ZHU ZHENCAI ; YAN WANZI ; LI YING ; ZHAO ZIYUAN ; ZHANG YIDONG ; YE JIMING</creator><creatorcontrib>LI XIAOQIANG ; ZHU ZHENCAI ; YAN WANZI ; LI YING ; ZHAO ZIYUAN ; ZHANG YIDONG ; YE JIMING</creatorcontrib><description>The invention discloses a mining unmanned electric locomotive speed control method based on an improved epsilon-greedy strategy and reinforcement learning, and the method comprises the steps: enabling a mining unmanned electric locomotive to serve as an intelligent agent, and setting the motion of the intelligent agent as an integer within the range of the optimal driving/braking torque applied to an axle by the electric locomotive; the method comprises the following steps: respectively designing actions, states and reward functions of an intelligent agent, and completing the construction of a main body algorithm structure for controlling the speed of the mining unmanned electric locomotive by taking real-time state information of the electric locomotive as input and taking driving/braking torque applied to an axle and a reward value obtained by the intelligent agent as output by utilizing a Q-Learning algorithm. The mining electric locomotive is ensured to autonomously complete behaviors such as safe car fol</description><language>chi ; eng</language><subject>CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION ; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES ; ELECTRIC EQUIPMENT OR PROPULSION OF ELECTRICALLY-PROPELLEDVEHICLES ; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES, IN GENERAL ; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES ; PERFORMING OPERATIONS ; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT ; TRANSPORTING ; VEHICLES IN GENERAL</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230714&DB=EPODOC&CC=CN&NR=116424341A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25563,76318</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230714&DB=EPODOC&CC=CN&NR=116424341A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LI XIAOQIANG</creatorcontrib><creatorcontrib>ZHU ZHENCAI</creatorcontrib><creatorcontrib>YAN WANZI</creatorcontrib><creatorcontrib>LI YING</creatorcontrib><creatorcontrib>ZHAO ZIYUAN</creatorcontrib><creatorcontrib>ZHANG YIDONG</creatorcontrib><creatorcontrib>YE JIMING</creatorcontrib><title>Mining unmanned electric locomotive speed control method based on improved epsilon-greedy strategy and reinforcement learning</title><description>The invention discloses a mining unmanned electric locomotive speed control method based on an improved epsilon-greedy strategy and reinforcement learning, and the method comprises the steps: enabling a mining unmanned electric locomotive to serve as an intelligent agent, and setting the motion of the intelligent agent as an integer within the range of the optimal driving/braking torque applied to an axle by the electric locomotive; the method comprises the following steps: respectively designing actions, states and reward functions of an intelligent agent, and completing the construction of a main body algorithm structure for controlling the speed of the mining unmanned electric locomotive by taking real-time state information of the electric locomotive as input and taking driving/braking torque applied to an axle and a reward value obtained by the intelligent agent as output by utilizing a Q-Learning algorithm. The mining electric locomotive is ensured to autonomously complete behaviors such as safe car fol</description><subject>CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION</subject><subject>CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES</subject><subject>ELECTRIC EQUIPMENT OR PROPULSION OF ELECTRICALLY-PROPELLEDVEHICLES</subject><subject>ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES, IN GENERAL</subject><subject>MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES</subject><subject>PERFORMING OPERATIONS</subject><subject>ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT</subject><subject>TRANSPORTING</subject><subject>VEHICLES IN GENERAL</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjTEKwkAURNNYiHqH7wFSxAR7CYqNVvZh3YxxYff_ZfcbSOHdjeABrAbevGGWxfvi2PFALw6GGT3Bw2pylrxYCaJuBOWIubHCmsRTgD6lp7vJMxQmF2KS8TuN2XnhckizPlHWZBTDRIZ7SnD8kGQRwEoeJn1v18XiYXzG5perYns63tpziSgdcjQWDO3aa1Xtm11TN9Wh_sf5AOf0ST8</recordid><startdate>20230714</startdate><enddate>20230714</enddate><creator>LI XIAOQIANG</creator><creator>ZHU ZHENCAI</creator><creator>YAN WANZI</creator><creator>LI YING</creator><creator>ZHAO ZIYUAN</creator><creator>ZHANG YIDONG</creator><creator>YE JIMING</creator><scope>EVB</scope></search><sort><creationdate>20230714</creationdate><title>Mining unmanned electric locomotive speed control method based on improved epsilon-greedy strategy and reinforcement learning</title><author>LI XIAOQIANG ; ZHU ZHENCAI ; YAN WANZI ; LI YING ; ZHAO ZIYUAN ; ZHANG YIDONG ; YE JIMING</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116424341A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION</topic><topic>CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES</topic><topic>ELECTRIC EQUIPMENT OR PROPULSION OF ELECTRICALLY-PROPELLEDVEHICLES</topic><topic>ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES, IN GENERAL</topic><topic>MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES</topic><topic>PERFORMING OPERATIONS</topic><topic>ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT</topic><topic>TRANSPORTING</topic><topic>VEHICLES IN GENERAL</topic><toplevel>online_resources</toplevel><creatorcontrib>LI XIAOQIANG</creatorcontrib><creatorcontrib>ZHU ZHENCAI</creatorcontrib><creatorcontrib>YAN WANZI</creatorcontrib><creatorcontrib>LI YING</creatorcontrib><creatorcontrib>ZHAO ZIYUAN</creatorcontrib><creatorcontrib>ZHANG YIDONG</creatorcontrib><creatorcontrib>YE JIMING</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LI XIAOQIANG</au><au>ZHU ZHENCAI</au><au>YAN WANZI</au><au>LI YING</au><au>ZHAO ZIYUAN</au><au>ZHANG YIDONG</au><au>YE JIMING</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Mining unmanned electric locomotive speed control method based on improved epsilon-greedy strategy and reinforcement learning</title><date>2023-07-14</date><risdate>2023</risdate><abstract>The invention discloses a mining unmanned electric locomotive speed control method based on an improved epsilon-greedy strategy and reinforcement learning, and the method comprises the steps: enabling a mining unmanned electric locomotive to serve as an intelligent agent, and setting the motion of the intelligent agent as an integer within the range of the optimal driving/braking torque applied to an axle by the electric locomotive; the method comprises the following steps: respectively designing actions, states and reward functions of an intelligent agent, and completing the construction of a main body algorithm structure for controlling the speed of the mining unmanned electric locomotive by taking real-time state information of the electric locomotive as input and taking driving/braking torque applied to an axle and a reward value obtained by the intelligent agent as output by utilizing a Q-Learning algorithm. The mining electric locomotive is ensured to autonomously complete behaviors such as safe car fol</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN116424341A |
| source | esp@cenet |
| subjects | CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES ELECTRIC EQUIPMENT OR PROPULSION OF ELECTRICALLY-PROPELLEDVEHICLES ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES, IN GENERAL MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES PERFORMING OPERATIONS ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT TRANSPORTING VEHICLES IN GENERAL |
| title | Mining unmanned electric locomotive speed control method based on improved epsilon-greedy strategy and reinforcement learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T22%3A27%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=LI%20XIAOQIANG&rft.date=2023-07-14&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN116424341A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |