机械工程学报
機械工程學報
궤계공정학보
Journal of Mechanical Engineering
2015年
19期
21-27
,共7页
张永顺%郭建超%王新%满达
張永順%郭建超%王新%滿達
장영순%곽건초%왕신%만체
三自由度球型腕%滑模控制%RBF神经网络%轨迹跟踪
三自由度毬型腕%滑模控製%RBF神經網絡%軌跡跟蹤
삼자유도구형완%활모공제%RBF신경망락%궤적근종
3-DOF spherical wrist%sliding mode control%RBF neural network%trajectory tracking
针对目前机械手腕结构复杂、集成度低、运动耦合、抗不确定因素“干扰”能力差等问题,提出一种由相对独立运动链组成的三自由度高集成解耦球型腕机构及其自适应滑模控制方法。球型腕采用双半球和双万向节的解耦结构保证腕关节的紧凑性与灵活性。分析运动传递关系,推导正、逆运动学方程及动力学方程,建立作业空间与关节空间的运动传递关系和系统控制模型,构建三自由度解耦球型腕试验平台。主动控制系统采用非线性Terminal滑膜控制器结合RBF神经网络算法评估不确定性上界,保证系统控制误差快速收敛,利用Lyapunov稳定性理论证明控制系统的稳定性。仿真和试验结果表明该控制方法对不确定性扰动不敏感,削弱滑模控制的“抖振”,能够快速、准确地跟踪轨迹,实现精确的定位控制。
針對目前機械手腕結構複雜、集成度低、運動耦閤、抗不確定因素“榦擾”能力差等問題,提齣一種由相對獨立運動鏈組成的三自由度高集成解耦毬型腕機構及其自適應滑模控製方法。毬型腕採用雙半毬和雙萬嚮節的解耦結構保證腕關節的緊湊性與靈活性。分析運動傳遞關繫,推導正、逆運動學方程及動力學方程,建立作業空間與關節空間的運動傳遞關繫和繫統控製模型,構建三自由度解耦毬型腕試驗平檯。主動控製繫統採用非線性Terminal滑膜控製器結閤RBF神經網絡算法評估不確定性上界,保證繫統控製誤差快速收斂,利用Lyapunov穩定性理論證明控製繫統的穩定性。倣真和試驗結果錶明該控製方法對不確定性擾動不敏感,削弱滑模控製的“抖振”,能夠快速、準確地跟蹤軌跡,實現精確的定位控製。
침대목전궤계수완결구복잡、집성도저、운동우합、항불학정인소“간우”능력차등문제,제출일충유상대독립운동련조성적삼자유도고집성해우구형완궤구급기자괄응활모공제방법。구형완채용쌍반구화쌍만향절적해우결구보증완관절적긴주성여령활성。분석운동전체관계,추도정、역운동학방정급동역학방정,건립작업공간여관절공간적운동전체관계화계통공제모형,구건삼자유도해우구형완시험평태。주동공제계통채용비선성Terminal활막공제기결합RBF신경망락산법평고불학정성상계,보증계통공제오차쾌속수렴,이용Lyapunov은정성이론증명공제계통적은정성。방진화시험결과표명해공제방법대불학정성우동불민감,삭약활모공제적“두진”,능구쾌속、준학지근종궤적,실현정학적정위공제。
To overcome some shortcomings of existing wrists, such as complex structure, low integration, kinematic coupling and poor ability to resist uncertain disturbance, a 3-DOF decoupling spherical wrist with independent kinematic chain and adaptive sliding mode control is proposed. The decoupling structure of double hemispheres and double universal joints is employed to guarantee compactness and flexibility of the spherical wrist. To analyze the transitive relation of the wrist, the forward and inverse kinematics and dynamics are derived. The system control model and motion transitive relation between the working space and the joint space are established, the experimental platform of the 3-DOF decoupled spherical wrist is established. A nonlinear terminal sliding surface controller and RBF neural network are employed to increase convergent speed of the system. The Lyapunov function is used to prove the stability of the control system. Simulation and experiment demonstrate that the proposed control method is insensitive to uncertain disturbance, the chattering of sliding mode system is reduced, trajectory tracking control ability is good, and higher positional accuracy is realized.