Terminal neural computing: Finite-time convergence and its applications

Abstract

To solve the time-varying Sylvester equations, a special kind of terminal neural networks (TNN) and its accelerated form are presented, which show better convergent behaviors of asymptotic ones. The terminal attraction of the matrix differential equations is analyzed, and the results show that the method can assure the networks of converging to zero during a limited period. The terminal neural networks can also be used to account for the time-varying matrix inversion as well as the trajectory planning of redundant manipulators. The typical example for a planar is the manipulator in which the end-effector appeared as a closed path, and the joint variables can return to the initial values, making the motion repeatable. The simulation results certify for the validity and superiority of the terminal neural method.