CN110456634A - 一种基于人工神经网络的无人车控制参数选取方法 - Google Patents
一种基于人工神经网络的无人车控制参数选取方法 Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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Cited By (5)
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WO2021238303A1 (zh) * | 2020-05-29 | 2021-12-02 | 华为技术有限公司 | 运动规划的方法与装置 |
CN113753060A (zh) * | 2020-10-30 | 2021-12-07 | 北京京东乾石科技有限公司 | 车辆的控制方法、装置、计算设备及介质 |
CN114475244A (zh) * | 2022-04-14 | 2022-05-13 | 禾多科技(北京)有限公司 | 定速巡航控制方法、整车控制器及定速巡航控制系统 |
CN114594747A (zh) * | 2022-01-30 | 2022-06-07 | 江苏华东特种车辆有限公司 | 车辆控制参数的标定系统 |
CN115406451A (zh) * | 2022-11-01 | 2022-11-29 | 联友智连科技有限公司 | 一种车辆定位方法、系统、车辆终端和存储介质 |
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CN108791289A (zh) * | 2018-04-28 | 2018-11-13 | 华为技术有限公司 | 一种车辆控制方法和装置 |
CN109094644A (zh) * | 2018-07-19 | 2018-12-28 | 南京航空航天大学 | 极限工况下的主动后轮转向与直接横摆力矩控制方法 |
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- 2019-07-01 CN CN201910582845.9A patent/CN110456634B/zh active Active
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JP2014100963A (ja) * | 2012-11-19 | 2014-06-05 | Hitachi Zosen Corp | 鉄道車両の位置検出装置および位置検出方法 |
CN103605285A (zh) * | 2013-11-21 | 2014-02-26 | 南京理工大学 | 汽车驾驶机器人系统的模糊神经网络控制方法 |
CN108791289A (zh) * | 2018-04-28 | 2018-11-13 | 华为技术有限公司 | 一种车辆控制方法和装置 |
CN109094644A (zh) * | 2018-07-19 | 2018-12-28 | 南京航空航天大学 | 极限工况下的主动后轮转向与直接横摆力矩控制方法 |
Non-Patent Citations (1)
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Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2021238303A1 (zh) * | 2020-05-29 | 2021-12-02 | 华为技术有限公司 | 运动规划的方法与装置 |
CN113805572A (zh) * | 2020-05-29 | 2021-12-17 | 华为技术有限公司 | 运动规划的方法与装置 |
CN113805572B (zh) * | 2020-05-29 | 2023-12-15 | 华为技术有限公司 | 运动规划的方法与装置 |
CN113753060A (zh) * | 2020-10-30 | 2021-12-07 | 北京京东乾石科技有限公司 | 车辆的控制方法、装置、计算设备及介质 |
CN113753060B (zh) * | 2020-10-30 | 2023-05-30 | 北京京东乾石科技有限公司 | 车辆的控制方法、装置、计算设备及介质 |
CN114594747A (zh) * | 2022-01-30 | 2022-06-07 | 江苏华东特种车辆有限公司 | 车辆控制参数的标定系统 |
CN114594747B (zh) * | 2022-01-30 | 2022-11-29 | 江苏华东特种车辆有限公司 | 车辆控制参数的标定系统 |
CN114475244A (zh) * | 2022-04-14 | 2022-05-13 | 禾多科技(北京)有限公司 | 定速巡航控制方法、整车控制器及定速巡航控制系统 |
CN115406451A (zh) * | 2022-11-01 | 2022-11-29 | 联友智连科技有限公司 | 一种车辆定位方法、系统、车辆终端和存储介质 |
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