@article{MAKHILLIJSSCEA20147428733,
    title = {Fuzzy Control of Flexible Serial Robots with Neural Tuner},
    journal = {International Journal of System Signal Control and Engineering Application},
    volume = {7},
    number = {4},
    pages = {61-69},
    year = {2014},
    issn = {1997-5422},
    doi = {ijssceapp.2014.61.69},
    url = {https://makhillpublications.co/view-article.php?issn=1997-5422&doi=ijssceapp.2014.61.69},
    author = {Seyed Mohammad Reza and},
    keywords = {Flexible robot,fuzzy logic controller,perceptron neural network,network,propagation learning},
    abstract = {In this study, trajectory tracking control of planar serial robots with the last flexible arm is studied.
The EOMs are derived using Lagrangian mechanics and the assumed modes method. The robot has a fuzzy
controller with neural tuner. The control system consists of a fuzzy logic controller in the feedback
configuration with error and change in error of the joints angular as input variables. Set parameters of
membership functions of inputs in fuzzification and output in defuzzification are fuzzy control challenges.
Utilizing a three-layer perceptron neural network a new method to estimate the on-line self-tuning parameters
of the membership functions is presented. In this method, symmetric triangular membership functions are used
in fuzzification and defuzzification units. Each of them is function of a productive parameter which is calculated
on-line using neural network. The network inputs depending on which membership function is set, can be
deformation, joint angular error or its derivative. The back propagation learning algorithm is used to update the
network weights and the biases. To validate the proposed method simulation is done and the results are
investigated.}
    }