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Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators 被引量:2

Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators
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摘要 A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme. A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme.
出处 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第11期2917-2925,共9页 中南大学学报(英文版)
基金 Project(61374051,61603387)supported by the National Natural Science Foundation of China Projects(20150520112JH,20160414033GH)supported by the Scientific and Technological Development Plan in Jilin Province of China Project(20150102)supported by Opening Funding of State Key Laboratory of Management and Control for Complex Systems,China
关键词 constrained reconfigurable manipulators position/force control model decomposition decentralized control neural network constrained reconfigurable manipulators position/force control model decomposition decentralized control neural network
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