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学者姓名：左国玉
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摘要 ：
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.
关键词 ：
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
引用：
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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.
关键词 ：
Configuration adjustment Configuration adjustment Dynamics modeling Dynamics modeling General robust controller General robust controller General energy function General energy function Control framework Control framework
引用：
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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.
关键词 ：
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)
引用：
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
自适应轨迹任务模仿的模仿学习方法研究
关键词 ：
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
引用：
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
新工科背景下基于机器人竞赛的创新人才培养模式
关键词 ：
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
引用：
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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
关键词 ：
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
引用：
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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.
关键词 ：
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
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| 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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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.
关键词 ：
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
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| 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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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。
引用：
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The aim of generative adversarial imitation learning (GAIL) is to allow an agent to learn an optimal policy from demonstrations via an adversarial training process. However, previous works have not considered a realistic setting for complex continuous control tasks such as robot manipulation, in which the available demonstrations are imperfect and possibly originate from different policies. Such a setting poses significant challenges for the application of the GAIL-related methods. This paper proposes a novel imitation learning (IL) algorithm, MD2-GAIL, to enable an agent to learn effectively from imperfect demonstrations by multiple demonstrators. Instead of training the policy from scratch, unsupervised pretraining is used to speed up the adversarial learning process. Confidence scores representing the quality of the demonstrations are utilized to reconstruct the objective function for off-policy adversarial training, making the policy match the optimal occupancy measure. Based on the Soft Actor Critic (SAC) algorithm, MD2-GAIL incorporates the idea of maximum entropy into the process of optimizing the objective function. Meanwhile, a reshaped reward function is adopted to update the agent policy to avoid falling into local optima.Experiments were conducted based on robotic simulation tasks, and the results show that our method can efficiently learn from the available demonstrations and achieves better performance than other state-of-the-art methods. (c) 2021 Elsevier B.V. All rights reserved.
关键词 ：
Adversarial imitation learning Adversarial imitation learning Imperfect demonstrations Imperfect demonstrations Multiple demonstrators Multiple demonstrators Robot learning Robot learning
引用：
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| GB/T 7714 | Zuo, Guoyu , Zhao, Qishen , Huang, Shuai et al. Adversarial imitation learning with mixed demonstrations from multiple demonstrators [J]. | NEUROCOMPUTING , 2021 , 457 : 365-376 . |
| MLA | Zuo, Guoyu et al. "Adversarial imitation learning with mixed demonstrations from multiple demonstrators" . | NEUROCOMPUTING 457 (2021) : 365-376 . |
| APA | Zuo, Guoyu , Zhao, Qishen , Huang, Shuai , Li, Jiangeng , Gong, Daoxiong . Adversarial imitation learning with mixed demonstrations from multiple demonstrators . | NEUROCOMPUTING , 2021 , 457 , 365-376 . |
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