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多自由度机器人自适应滑模迭代学习跟踪控制

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机械臂可代替人工完成繁重工作、降低生产风险、提高生产效率，被广泛应用于制造业和生产业中。工业生产的高质量需求对机械臂的操作精度提出了较高要求，考虑有色金属工厂中铸锭打磨的应用场景，该任务的作业轨迹均具有较强重复性。此外，实际应用环境复杂，存在如环境干扰及系统参数变化等多种不确定性，固定的控制参数难以保证系统持续稳定运行。基于上述考虑，针对多自由度机械臂系统，设计一种自适应滑模迭代学习跟踪控制方法，控制器包含参数自整定的比例-微分项、基于滑模的符号函数项和上一次迭代的控制输入，其中PD项的控制参数通过模糊逻辑系统实时调整，在保证控制系统正常运行的情况下提高系统的鲁棒性。同时，在理论上证明迭代域闭环系统的稳定性和跟踪误差的收敛性。最后通过仿真验证所提出控制方法的有效性和鲁棒性。
Adaptive sliding mode iterative learning tracking control of multi-degree-of-freedom robots
Manipulators are widely used in manufacturing to replace manual work,reduce production risk,and improve production efficiency.However,the high-quality demand of industrial production puts forward specific requirements for the operation accuracy of manipulators.Considering the application scenario of ingot grinding in non-ferrous metal factories,the operation tracks of the task are highly repetitive.In addition,the practical application environment is complex and there are many uncertainties such as environmental interferences and system parameter changes,which result in that fixed control parameters are difficult to ensure the continuous and stable operation of the system.Therefore,an adaptive sliding mode iterative learning tracking control method is designed in this paper.The controller includes a PD term with parameter self-tuning,the symbolic function term based on sliding mode,and a control input of the last iteration.Especially,the PD control parameters are adjusted by the fuzzy logic system to improve the robustness of the system.Furthermore,the closed-loop stability is proven by using Lyapunov techniques.Finally,the effectiveness and robustness of the proposed control method are verified through several numerical simulations.

multi-degree-of-freedom(DOF)manipulatoriterative learning controlsliding mode controlfuzzy adaptive controlparameter self-tuningtrajectory tracking control

张程琳、桑文闯、孙宁、邱泽昊、吴庆祥、方勇纯

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中国飞机强度研究所,西安 710065

南开大学人工智能学院,天津 300350

南开大学深圳研究院智能技术与机器人系统研究院,广东深圳 518083

多自由度机械臂 迭代学习控制 滑模控制 模糊自适应控制 参数自整定 轨迹跟踪控制

国家重点研发计划国家自然科学基金广东省基础与应用基础研究基金中国博士后科学基金

2018YFB1309000522050192023A15150126692021M701779

2024

10.13195/j.kzyjc.2022.1923

控制与决策
东北大学

控制与决策

CSTPCD北大核心
影响因子：1.227
ISSN：1001-0920
年,卷(期)：2024.39(6)