信息与控制2025，Vol.54Issue(2)：276-287,298,13.DOI:10.13976/j.cnki.xk.2024.4095
考虑全状态约束和扰动的机械臂神经网络控制
Neural Network Control of Robotic Arm Considering Full-state Constraints and Disturbances
摘要
Abstract
To address the tracking control challenges of robotic arms faced with full state constraints,bounded unknown disturbances,and dynamic uncertainties,we propose an adaptive neural network control strategy that employs tangent-type barrier Lyapunov functions to manage full state con-straints and bounded disturbances.Time-varying constraints are applied to position errors,while static constraints handle velocity errors.A time-varying class PD term is introduced in the virtual control design to speed up system response.To address and suppress the bounded disturbances caused when the end of the robotic arm carries an object without fixing it well,an adaptive neural network is used.This approach effectively deals with system uncertainties,ensuring that the robot-ic arm satisfies predefined state constraints even under external disturbances and unknown dynamics.The Moore-Penrose inverse and Lyapunov stability theory are introduced to prove that the closed-loop system remains consistently bounded.Comparative simulation results demonstrate the method's advantages in achieving fast response speeds,small tracking errors,and strong ro-bustness to full-state constraints.Experimental results on a Franka Emika Panda robot validate the effectiveness of the proposed method.关键词
障碍李雅普诺夫函数/全状态约束/有界扰动/自适应神经网络Key words
barrier Lyapunov function/full-state constraint/bounded disturbance/adaptive neural network分类
计算机与自动化引用本文复制引用
艾文旭,姜勇,潘新安,王洪光..考虑全状态约束和扰动的机械臂神经网络控制[J].信息与控制,2025,54(2):276-287,298,13.基金项目
国家自然科学基金项目(52075531,U20A20282) （52075531,U20A20282）
辽宁省国际科技合作计划(2023JH2/10700008) （2023JH2/10700008）
辽宁省应用基础研究计划(2022JH2/101300205) （2022JH2/101300205）
