李彬

简介            

研究领域         

神经网络；足式机器人控制算法；机器人智能控制

主讲课程                                                                                                            

《机器人技术基础》、《神经网络》、《数学建模》

学术兼职                                                                                                            

1、2022年至今，山东省自动化学会常务理事。

论文、著作(*表示通讯作者)                          

[1] Li Bin, Yang Xue, Zhou Ranran, et al. Reinforcement learning-based optimised control for a class of second-order nonlinear dynamic systems[J]. International Journal of Systems Science, 2022: 1-11.

[2] Li Bin, Zhu Jiahao, Zhou Ranran, et al. Adaptive Neural Network Sliding Mode Control for a Class of SISO Nonlinear Systems[J]. Mathematics, 2022, 10(7): 1182.

[3] Wen Guoxing, Li Bin, Niu Ben. Optimized Backstepping Control Using Reinforcement Learning of Observer-Critic-Actor Architecture Based on Fuzzy System for a Class of Nonlinear Strict-Feedback Systems[J]. IEEE Transactions on Fuzzy Systems, 2022: 4322-4335.

[4] Zhu Jiahao, Wen Guoxing, Li Bin. Decentralized adaptive formation control based on sliding mode strategy for a class of second‐order nonlinear unknown dynamic multi‐agent systems[J]. International Journal of Adaptive Control and Signal Processing, 2022, 36(4): 1045-1058.

[5] Zhou Ranran, Wen Guoxing, Li Bin. Reinforcement learning‐based optimized backstepping control of nonlinear strict feedback system with unknown control gain function[J]. Optimal Control Applications and Methods, 2022;43(5):1358-1378.

[6] Qiao Fengjuan, Li Bin*, Li Jiangjiao. ECG signal classification based on adaptive multi-channel weighted neural network[J]. Neural Network World, 2022, 32(1): 55.

[7] Li Zhi, Li Bin*, Liang Q, et al. A quadruped robot obstacle avoidance and personnel following strategy based on ultra-wideband and three-dimensional laser radar[J]. International Journal of Advanced Robotic Systems, 2022, 19(4): 17298806221114705.

[8] Hou Landong, Li Bin*, Liu Weilong, et al. Deep Reinforcement Learning for Model Predictive Controller Based on Disturbed Single Rigid Body Model of Biped Robots[J]. Machines, 2022, 10(11): 975.

[9] Gao Mengqi, Li J, Zhou Dazheng, Zhi Yumin, Zhang Mingliang, Li Bin*. Fall detection based on OpenPose and MobileNetV2 network[J]. IET Image Processing, 2022.

[10] Wen Guoxing, Dou Hui, Li Bin. Adaptive fuzzy leader‐follower consensus control using sliding mode mechanism for a class of high‐order unknown nonlinear dynamic multi‐agent systems[J]. International Journal of Robust and Nonlinear Control, 2023, 33(1): 545-558.

[11] Li Jiangjiao, Gao Mengqi, Li Bin*, et al. KAMTFENet: a fall detection algorithm based on keypoint attention module and temporal feature extraction[J]. International Journal of Machine Learning and Cybernetics, 2022: 1-14.

[12] Yang Xue, Li Bin*. Optimized tracking control using reinforcement learning strategy for a class of nonlinear systems[J]. Asian Journal of Control. 2022;1-10.

[13] Weilong Liu, Bin Li*, Landong Hou, et al. A Motion Planning and Control Method of Quadruped Robot Based on Deep Reinforcement Learning. 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2022:2003-2008，EI.

[14] Qixing Liang, Bin Li*, Yiming Xu, et al. Vision-based Dynamic Gait Stair Climbing Algorithm for Quadruped Robot. 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2022:1390-1395，EI.

[15] Wen Guoxing, Li Bin. Optimized Leader-Follower Consensus Control Using Reinforcement Learning for a Class of Second-Order Nonlinear Multiagent Systems[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 52(9): 5546-5555.

[16] Wen Guoxing, Xu Liguang, Li Bin. Optimized Backstepping Tracking Control Using Reinforcement Learning for a Class of Stochastic Nonlinear Strict-Feedback Systems[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021:1-13.

[17] 梁启星, 李彬*, 李志, 等. 基于模型预测控制的四足机器人斜坡自适应调整算法与实现[J]. 山东大学学报(工学版), 2021, 51(3): 37-44.

[18] 辛亚先， 李贻斌，李彬*，荣学文，四足机器人静-动步态平滑切换算法， 山东大学学报（工学版）, 2018.8.15, 48(4): 42~49.

[19] Li Bin, Li Yibin, Rong Xuewen, The extreme learning machine learning algorithm with tunable activation function, Neural Computing & Applications, 2013, 22(3-4):531-539

科研项目

[1] 山东省自然科学基金面上项目，ZR2020MF097，动态非结构环境中四足机器人自主稳定运动控制方法研究及应用，2021/01-2023/12,10万元，1/10、负责人

[2] 济南市“新高校20条”项目，2021GXRC100，机器人智能环境感知与自主学习算法研究及应用，2022/01-2023/12, 20万元，1/8、负责人

[3] 山东省高等学校“青创科技计划”, 2019KJN011,机器人智能环境感知与自主学习算法研究及应用,2020/01-2022/12, 18万元,1/7、负责人

[4] 国家自然科学基金面上基金项目, 61973185,仿鸵鸟机器人高能效机构设计及运动规划与控制方法研究,2020/01-2023/12,23万元,2/10、负责人

[5] 山东省重点研发计划, 2018GGX103054,基于复杂环境动态交互的机器人智能自主稳定行走关键技术研究,2018/05-2020/12, 20万元,1/10、负责人

[6] 国家自然科学基金面上基金项目,61773226,面向复杂环境的四足机器人自适应和快速稳定运动控制方法研究及应用,2018/01-2018/12,16万元,1/10、负责人

[7] 国家自然科学基金青年科学基金项目,61305130,高动态四足机器人步态规划和优化与稳定运动控制方法研究,2014/01-2016/12,23万元,1/10、负责人

[8] 山东省自然科学基金青年基金项目,ZR2013FQ003,高动态四足机器人步态规划和稳定运动控制方法研究,2013/10-2016/10,6万元,1/10、负责人

授权发明专利                                                      

[1] 李彬；侯兰东；刘伟龙；徐一明；杨淑慧；刘丽霞，一种基于深度强化学习双足机器人运动控制方法及系统，中国专利，专利号：ZL202211050051.6

[2]李彬；刘伟龙；侯兰东；杨淑慧；徐一明；张友梅；张瑜；张明亮，一种四足机器人运动控制方法、系统、存储介质及设备，中国专利，专利号：ZL202210512279.6

[3]张友梅；李江娇；李彬；高梦奇；智昱旻；周大正；张明亮；张瑜，基于时空信息的跌倒检测方法、系统、存储介质及设备，中国专利，专利号：ZL202210536743.5

[4]刘丽霞；刘伟龙；郭荣伟；李彬；张瑜；侯兰东，一种多柔性机械臂系统自适应一致性追踪控制方法及系统，中国专利，专利号：ZL202110271297.5

[5]张友梅；李彬；张瑜，一种人群密度估计方法及系统，中国专利，专利号：ZL202010449640.6

[6]李彬；张慧；李贻斌；荣学文；刘斌，基于A*算法的伴随机器人增量路径规划方法及系统, 中国专利, 专利号：ZL201810825064.3

[7]李彬； 辛亚先；张慧；李贻斌；荣学文； 郭荣伟，应用于四足仿生机器人的静步态和对角小跑步态切换算法,中国专利, 专利号：ZL201610861666.5.