Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network - Cao, Yuxuan; Liu, Boyun; Pu, Jinyun (Frontiers in Neurorobotics, 2023)
A zeroing neural network activated by nonlinear functions is employed to control tracked mobile robot to track desired trajectory. A new fractional exponential activation function is designed in the paper, and the implicit derivative dynamic model of the tracked mobile robot is presented, termed finite-time convergence zeroing neural network. The proposed model is analyzed based on the Lyapunov stability theory, and the upper bound of the convergence time is given. In addition, the robustness of finite-time convergence zeroing neural network model is investigated under different error disturbances. Numerical experiments of tracking an 8-shaped trajectory are conducted successfully, validating the proposed model for the trajectory tracking problem of tracked mobile robots. Comparative results validate the effectiveness and superiority of the proposed model for the kinematical resolution of tracked mobile robot even in a disturbance environment.