In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying t...In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.展开更多
针对一种气动人工肌肉驱动的弹簧质量位置控制系统,设计了一个带有自适应模糊小脑模型(Cerebellar Model Articulation Controller,CMAC)在线逼近的离散趋近律滑模混合控制器.该混合控制器中离散趋近律滑模策略产生控制器的输出;自适应...针对一种气动人工肌肉驱动的弹簧质量位置控制系统,设计了一个带有自适应模糊小脑模型(Cerebellar Model Articulation Controller,CMAC)在线逼近的离散趋近律滑模混合控制器.该混合控制器中离散趋近律滑模策略产生控制器的输出;自适应模糊CMAC用以逼近气动人工肌肉系统中的不确定项.CMAC网络权值的在线学习调整保证了自适应模糊CMAC的逼近性能.对离散抗饱和PID控制器(DASPID)与自适应模糊CMAC离散滑模混合控制器(HybridC)的位置跟踪控制性能进行了对比实验.实验结果表明,HybridC较之DASPID有更好的位置跟踪控制性能.当期望参考输入为正弦信号时,DASPID的最大位置跟踪误差为±1.5 mm;而HybridC的最大位置跟踪误差仅为±0.7 mm,平均位置跟踪误差大约仅为±0.2 mm.并且,离散滑模所固有的抖振现象得到了有效的抑制.展开更多
基金supported by the National Natural Science Foundation of China(Nos.6117075,60835004)the National High Technology Research and Development Program of China(863 Program)(Nos.2012AA111004,2012AA112312)
文摘In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.
