As are considered, the body posture is controlled and position cannot control, space manipulator system model is difficult to be set up because of disturbance and model uncertainty. An adaptive control strategy based on neural network is put forward. Neural network on-line modeling technology is used to approximate the system uncertain model, and the strategy avoids solving the inverse Jacobi matrix, neural network approximation error and external bounded disturbance are eliminated by variable structure control controller. Inverse dynamic model of the control strategy does not need to be estimated, also do not need to take the training process, globally asymptotically stable of the closed-loop system is proved based on the lyapunov theory. The simulation results show that the designed controller can achieve high control precision has the important value of engineering application.


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    Title :

    Adaptive Control of Space Robot Manipulators with Task Space Base on Neural Network


    Contributors:
    Shuhua, Zhou (author) / Xiaoping, Ye (author) / Xiaoming, Ji (author) / Wenhui, Zhang (author)

    Publication date :

    2014-06-01


    Remarks:

    doi:10.12928/telkomnika.v12i2.72
    TELKOMNIKA (Telecommunication Computing Electronics and Control); Vol 12, No 2: June 2014; 349-356 ; 2302-9293 ; 1693-6930 ; 10.12928/telkomnika.v12i2



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




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