An Exponential Varying-Parameter Neural Network for Repetitive Tracking of Mobile Manipulators

Ying Kong, Qingqing Tang, Jingsheng Lei, Ruiyang Zhang
2020 Complexity  
A novel exponential varying-parameter neural network (EVPNN) is presented and investigated to solve the inverse redundancy scheme of the mobile manipulators via quadratic programming (QP). To suspend the phenomenon of drifting free joints and guarantee high convergent precision of the end effector, the EVPNN model is applied to trajectory planning of mobile manipulators. Firstly, the repetitive motion scheme for mobile manipulators is formulated into a QP index. Secondly, the QP index is
more » ... QP index is transformed into a time-varying matrix equation. Finally, the proposed EVPNN method is used to solve the QP index via the matrix equation. Theoretical analysis and simulations illustrate that the EVPNN solver has an exponential convergent speed and strong robustness in mobile manipulator applications. Comparative simulation results demonstrate that the EVPNN possesses a superior convergent rate and accuracy than the traditional ZNN solver in repetitive trajectory planning with a mobile manipulator.
doi:10.1155/2020/8520835 doaj:0ed7acab475b4e93a3d7f83441a74df1 fatcat:nbj5h3shyzaodckrxjlrzz2iue