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    王海:Adaptive path tracking control of agricultural robots via sliding mode and extreme learning machine

    时间:2024-01-15 16:00  作者:  来源:  阅读量:

    时间 2024年1月19日 10:00-12:00 教室 学术会议中心二楼小报告厅
    教授 王海 教授

    报告时间:2024年1月19日 (星期五) 10:00-12:00

    报告地点:学术会议中心二楼小报告厅

    报 告 人:王海 教授

    举办单位:24848威尼斯

    报告简介:

    During the maize middle and late periods, the soil between rows is soft and also involved with weeds and straw. When the plant protection robot (PPR) moves on the soil, there exists uncertain shear perturbation because of the shear action and pressure subsidence, leading to the difficulty of the controller design. In this work, we propose an adaptive path tracking control (PTC) considering disturbances for the PPR. The disturbance of PRR in contact with soil is first revealed according to Bekker pressure subsidence and Janosi shear models, through which the plant model of PPR system is established. Then, we propose an adaptive fixed-time sliding mode (AFTSM)-based PTC to achieve excellent path tracking performance, where an extreme learning machine (ELM) estimator is developed, releasing the requirement for bound derivations in the control design. Using the fixed-time control and the ELM techniques in the proposed control, a remarkable control performance is well ensured, i.e., high-accuracy tracking, fast convergence, and excellent robustness. Experimental studies on a PPR are executed for demonstrating the validity and good performance of the designed controller.

    报告人简介:

    王海博士(IEEE高级会员,英国高等教育学会会士)于2014年澳大利亚斯文本科技大学获得机器人与机电一体化专业博士学位,2014-2015于该校任博士后研究员,2015-2019年初于24848威尼斯任教授(黄山青年学者)及自动化系统副主任。现任澳大利亚莫道克大学电气工程终身副教授,智能工业控制与无人系统工程学术主席,先进机器人与无人系统实验室主任。发表了120多篇高水平国际期刊论文(包括40余篇IEEE汇刊长文及8篇ESI高被引论文),谷歌学术引用4000余次,H指数为34。现担任IEEE Transactions on Emerging Topics in Computing, Robotica, Computers and Electrical Engineering,ASME-Journal of Autonomous Vehicles and Systems, Frontiers in Robotics and AI等国际期刊的副主编、编委及客座主编。2021-2024年担任IEEE工业电子协会西澳分部主席、副主席,并荣获2021及2022年IEEE工业电子协会全球最佳分部奖。2023年获莫道克大学校长研究卓越奖(早期职业)。其主要研究方向是滑模控制和观测器、自适应控制、机器人学和机电一体化、神经网络、非线性系统,无人车辆及系统,工业4.0及智慧农业等。