Finitely-valued Terminal Zeroing Neural Networks with Application to Robotic Motion Planning

To solve the problem of time-varying quadratic programming with equality constraints,this paper proposes a finitely-valued terminal zeroing neural network achieving the finite-time convergence of computing errors while being easy to implement.The convergence of the finitely-valued terminal zeroing neural network is theoretically analyzed,and the specific expression for settling time is provided.The repetitive motion planning problem of redundant robotic manipulators can be described as a time-varying quadratic programming.By employing the finitely-valued terminal zeroing neural network as a solver,the joint position and velocity trajectories,corresponding to the desired end-effector trajectory,can be obtained.Considering the inevitable initial joint shift,a terminal optimization criterion with fixed/adaptive parameters is proposed,aiming for finite-time convergence of end-effector position error and higher precision in repetitive motion planning.To ensure smooth operation of the robotic manipulator,a smooth-corrected finite-value function is proposed for the terminal optimization criterion,and the finite-time convergence of the end-effector position error is established.Numerical simulations and UR5 manipulator simulation and experimental results validate effectiveness of the proposed computing scheme.