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学者姓名：左国玉
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Abstract ：
Intelligent controllers based on the broad learning system can simplify the process of model parameter adjustment, finding wide applications in the motion control of multi-joint robotic arms. However, motion controllers for multi-joint robotic arms based on broad learning system exhibit insufficient precision and overlook the impact of joint motion commonalities on controller design. Therefore, this paper proposes a novel motion control strategy for a multi-joint robotic arm based on a deep cascade feature-enhancement gated Bayesian broad learning system. Firstly, the motion controller of the deep cascade feature-enhancement Bayesian broad learning system is constructed to enhance the robotic arm motion control precision. Secondly, an incremental node generation module with an attention-gated mechanism is constructed to capture the unique motion characteristics of the target joints, which is further combined with model generalization to simplify the motion control process of the multi-joint robotic arm. Finally, controller convergence is enhanced by combining it with the Lyapunov theory to constrain the learning parameters. Simulations and physical experiments are designed to verify the feasibility and superiority of the proposed motion control strategy. The results demonstrated that the strategy improved the accuracy of robotic arm motion control, with the root mean square error in position tracking reduced to 0.0019 rad. This represents a 93.39% reduction in error compared to existing techniques.
Keyword ：
Attention-gated mechanism Attention-gated mechanism Intelligent manufacturing Intelligent manufacturing Broad learning system Broad learning system Multi-joint robotic arm control Multi-joint robotic arm control Motion controller Motion controller
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| GB/T 7714 | Zhou, Jiyong , Zuo, Guoyu , Yu, Shuangyue et al. Motion controller for multi-joint robotic arm with deep cascade gated Bayesian broad learning system [J]. | APPLIED MATHEMATICAL MODELLING , 2025 , 138 . |
| MLA | Zhou, Jiyong et al. "Motion controller for multi-joint robotic arm with deep cascade gated Bayesian broad learning system" . | APPLIED MATHEMATICAL MODELLING 138 (2025) . |
| APA | Zhou, Jiyong , Zuo, Guoyu , Yu, Shuangyue , Dong, Shuaifeng , Liu, Chunfang . Motion controller for multi-joint robotic arm with deep cascade gated Bayesian broad learning system . | APPLIED MATHEMATICAL MODELLING , 2025 , 138 . |
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Abstract ：
Existing robot control models suffer from poor generalization performance due to varied tasks between manipulators, configuration differences, and physical limits of motion. To address this problem, a multiplatform migration control framework (MPMC-frame) based on a constrained dynamics model is proposed in this paper. First, a model of the robotic manipulator dynamics with configuration adjustment (CA) is constructed based on a neural network to unify the physical joint data of different manipulators. Second, the model -based controller that can be integrated into the framework is designed, and the constraint on the upper bound on the error of uncertain parameters in the control law to guarantee the control robustness and accuracy of the controller for different manipulator platforms. Final, the generic potential function is designed based on multiplatform task requirements, and a task parameter table is constructed to improve the joint motion control performance of MPMC-frame. The feasibility of the method proposed in this paper are verified through simulations and experiments based on the ABB_irb120 and AUBO_i5 robotic manipulators.
Keyword ：
Configuration adjustment Configuration adjustment Dynamics modeling Dynamics modeling General robust controller General robust controller General energy function General energy function Control framework Control framework
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| GB/T 7714 | Zuo, Guoyu , Zhou, Jiyong , Liu, Lu et al. MPMC-frame: Multiplatform migration control framework for manipulator control [J]. | CONTROL ENGINEERING PRACTICE , 2024 , 145 . |
| MLA | Zuo, Guoyu et al. "MPMC-frame: Multiplatform migration control framework for manipulator control" . | CONTROL ENGINEERING PRACTICE 145 (2024) . |
| APA | Zuo, Guoyu , Zhou, Jiyong , Liu, Lu , Gong, Daoxiong . MPMC-frame: Multiplatform migration control framework for manipulator control . | CONTROL ENGINEERING PRACTICE , 2024 , 145 . |
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Abstract ：
Intelligent servo control significantly reduces the need to adjust control parameters, and is, therefore, widely used in robot joint control. However, existing intelligent servo control strategies for robot joints have problems of computational redundancy, limited prediction accuracy, and insufficient generalization capability. To solve these problems, this article proposes a servo control strategy for robot joints that is based on the incremental Bayesian fuzzy broad learning system (IBFBLS). First, we construct an intelligent servo control strategy with broad learning system on the basis of fuzzy rules to achieve good self-learning and generalization abilities. Second, the learning parameters of the control strategy are optimized by Bayesian inference to achieve precise joint servo control. Finally, the convergence of the control strategy is enhanced by combining it with Lyapunov theory to constrain the learning parameters of the proposed control strategy. The feasibility and superiority of the proposed control strategy are verified by simulation to compare it with existing intelligent servo con-trol methods. In addition, experiments are conducted using robot joint test bed. Both the simulation and experiments verify that the proposed servo control strategy outperforms other servo control methods with respect to tracking ac-curacy, stability, and convergence. The root-mean-square error in servo control of robot joints was 0.012%, which has been reduced by 55.56% compared with the current state of the art.
Keyword ：
Lyapunov theory Lyapunov theory Bayesian inference Bayesian inference fuzzy rules fuzzy rules intelligent servo control intelligent servo control incremental broad learning system (IBLS) incremental broad learning system (IBLS)
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| GB/T 7714 | Zuo, Guoyu , Zhou, Jiyong , Gong, Daoxiong et al. Intelligent Servo Control Strategy for Robot Joints With Incremental Bayesian Fuzzy Broad Learning System [J]. | IEEE-ASME TRANSACTIONS ON MECHATRONICS , 2023 , 28 (4) : 2029-2037 . |
| MLA | Zuo, Guoyu et al. "Intelligent Servo Control Strategy for Robot Joints With Incremental Bayesian Fuzzy Broad Learning System" . | IEEE-ASME TRANSACTIONS ON MECHATRONICS 28 . 4 (2023) : 2029-2037 . |
| APA | Zuo, Guoyu , Zhou, Jiyong , Gong, Daoxiong , Huang, Gao . Intelligent Servo Control Strategy for Robot Joints With Incremental Bayesian Fuzzy Broad Learning System . | IEEE-ASME TRANSACTIONS ON MECHATRONICS , 2023 , 28 (4) , 2029-2037 . |
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Abstract ：
自适应轨迹任务模仿的模仿学习方法研究
Keyword ：
机器人 机器人 性能评价 性能评价 动态系统 动态系统 非线性函数 非线性函数 模仿学习 模仿学习
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| GB/T 7714 | 于建均 , 姚红柯 , 左国玉 et al. 自适应轨迹任务模仿的模仿学习方法研究 [J]. | 于建均 , 2021 , 28 (2) : 266-274 . |
| MLA | 于建均 et al. "自适应轨迹任务模仿的模仿学习方法研究" . | 于建均 28 . 2 (2021) : 266-274 . |
| APA | 于建均 , 姚红柯 , 左国玉 , 阮晓钢 , 控制工程 . 自适应轨迹任务模仿的模仿学习方法研究 . | 于建均 , 2021 , 28 (2) , 266-274 . |
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Abstract ：
新工科背景下基于机器人竞赛的创新人才培养模式
Keyword ：
机器人竞赛 机器人竞赛 创新人才 创新人才 培养模式 培养模式 新工科 新工科
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| GB/T 7714 | 左国玉 , 雷飞 , 乔俊飞 et al. 新工科背景下基于机器人竞赛的创新人才培养模式 [J]. | 左国玉 , 2021 , (6) : 44-47 . |
| MLA | 左国玉 et al. "新工科背景下基于机器人竞赛的创新人才培养模式" . | 左国玉 6 (2021) : 44-47 . |
| APA | 左国玉 , 雷飞 , 乔俊飞 , 高教学刊 . 新工科背景下基于机器人竞赛的创新人才培养模式 . | 左国玉 , 2021 , (6) , 44-47 . |
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Abstract ：
新工科背景下面向创新能力培养的微机原理与应用课程改革
Keyword ：
课程改革 课程改革 高等教育 高等教育 微机原理与应用 微机原理与应用 创新人才培养 创新人才培养 新工科 新工科
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| GB/T 7714 | 左国玉 , 雷飞 , 乔俊飞 et al. 新工科背景下面向创新能力培养的微机原理与应用课程改革 [J]. | 左国玉 , 2021 , (2) : 108-112 . |
| MLA | 左国玉 et al. "新工科背景下面向创新能力培养的微机原理与应用课程改革" . | 左国玉 2 (2021) : 108-112 . |
| APA | 左国玉 , 雷飞 , 乔俊飞 , 计算机教育 . 新工科背景下面向创新能力培养的微机原理与应用课程改革 . | 左国玉 , 2021 , (2) , 108-112 . |
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Abstract ：
机器人模仿的学习方法在行为运动的模仿上受到示教速度的限制,导致机器人模仿的速度也受到限制,无法更好发挥机器人的性能.为了提高机器人行为模仿的快速性,提出了一种自适应改变机器人模仿学习运动速度的方法.首先通过基于动态系统的方法建模示教运动,并将动态系统稳定的充分条件作为约束,确保行为模仿的稳定性.其次构造了一个随机器人状态到目标点的距离而变化的非线性函数,将非线性函数作为参数与系统模型结合,以便自适应地调整模仿的速度.最后给出了4种模仿学习评价的方法来评价模仿的性能.实验结果表明,提出的方法在保证机器人运动模仿的稳定性前提下很好地提高了行为模仿的速度.
Keyword ：
机器人 机器人 动态系统 动态系统 性能评价 性能评价 模仿学习 模仿学习 非线性函数 非线性函数
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| GB/T 7714 | 于建均 , 姚红柯 , 左国玉 et al. 自适应轨迹任务模仿的模仿学习方法研究 [J]. | 控制工程 , 2021 , 28 (2) : 266-274 . |
| MLA | 于建均 et al. "自适应轨迹任务模仿的模仿学习方法研究" . | 控制工程 28 . 2 (2021) : 266-274 . |
| APA | 于建均 , 姚红柯 , 左国玉 , 阮晓钢 . 自适应轨迹任务模仿的模仿学习方法研究 . | 控制工程 , 2021 , 28 (2) , 266-274 . |
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Abstract ：
分析传统工科教学存在的问题,提出多角度融合式课程教学改革,给出教学改革框架,并以微机原理与应用为例,从教学软件、教学硬件、实验环节和考核环节详细介绍课程改革措施,最后以课程改革后学生取得的创新成果说明教学改革框架的有效性.
Keyword ：
创新人才培养 创新人才培养 微机原理与应用 微机原理与应用 新工科 新工科 课程改革 课程改革 高等教育 高等教育
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| GB/T 7714 | 左国玉 , 雷飞 , 乔俊飞 . 新工科背景下面向创新能力培养的微机原理与应用课程改革 [J]. | 计算机教育 , 2021 , (2) : 108-112 . |
| MLA | 左国玉 et al. "新工科背景下面向创新能力培养的微机原理与应用课程改革" . | 计算机教育 2 (2021) : 108-112 . |
| APA | 左国玉 , 雷飞 , 乔俊飞 . 新工科背景下面向创新能力培养的微机原理与应用课程改革 . | 计算机教育 , 2021 , (2) , 108-112 . |
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Abstract ：
一种七连杆双足机器人的搭建以及基于ZMP和CPG的混合控制方法，属于人工智能与机器人控制领域。本发明设计了一款小型双足机器人，包含控制模块、驱动模块、传感器模块、电源模块，其具有开源性强，可以自己搭建底层驱动，可以充分进行环境感知的特点，并基于该机器人提出了一种ZMP和CPG的混合控制方法。通过机器人的压力传感器模块测量并计算得出机器人的零力矩点，将实际零力矩点与预期零力矩点的偏差，作为补偿反馈给开环的CPG控制当中，实现系统的闭环控制，增加行走稳定性。本发明能够让双足机器人具有更强大的开源性以及环境感知能力，并且其混合控制方法可以使机器人在避免巨大计算量的前提下实现机器人行走系统的闭环控制。
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| GB/T 7714 | 于建均 , 刘一忻 , 左国玉 et al. 一种七连杆双足机器人的设计以及基于ZMP和CPG的混合控制方法 : CN202110415227.2[P]. | 2021-04-18 . |
| MLA | 于建均 et al. "一种七连杆双足机器人的设计以及基于ZMP和CPG的混合控制方法" : CN202110415227.2. | 2021-04-18 . |
| APA | 于建均 , 刘一忻 , 左国玉 , 李瑞琪 , 刘鹏 , 曹艺琳 . 一种七连杆双足机器人的设计以及基于ZMP和CPG的混合控制方法 : CN202110415227.2. | 2021-04-18 . |
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Abstract ：
为了让机器人获得更加通用的能力,抓取是机器人必要掌握的技能.针对目前大多数机器人抓取决策方法存在物品特征理解浅显,缺乏抓取先验知识,导致任务兼容性较差的问题,同时受大脑中分区分块功能结构的启发,提出了将物品感知、先验知识和抓取任务融合的认知决策模型.该模型包含卷积感知网络、记忆图网络和贝叶斯决策网络三部分,分别实现了物品能供性(affordance)提取、抓取先验知识推理和联想,以及信息融合编码决策,三部分之间的信息流以语义向量的形式传递.利用UMD part affordance数据集、该文构建的抓取常识图和决策数据集对3个网络分别进行训练,认知决策模型的测试准确率达到99.8％,并且抓取位置可视化结果展示了决策的正确性.该模型还能判断物品是否属于当前任务场景,以决策是否抓取以及选择什么部位抓取物品,有助于提高机器人实际场景的应用能力.
Keyword ：
记忆图 记忆图 认知模型 认知模型 机器人抓取 机器人抓取 决策模型 决策模型 脑启发 脑启发 物品感知 物品感知
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| GB/T 7714 | 左国玉 , 刘洪星 , 龚道雄 et al. 受脑启发的机器人认知抓取决策模型 [J]. | 北京工业大学学报 , 2021 , 47 (8) : 863-873 . |
| MLA | 左国玉 et al. "受脑启发的机器人认知抓取决策模型" . | 北京工业大学学报 47 . 8 (2021) : 863-873 . |
| APA | 左国玉 , 刘洪星 , 龚道雄 , 阮晓钢 . 受脑启发的机器人认知抓取决策模型 . | 北京工业大学学报 , 2021 , 47 (8) , 863-873 . |
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