Deterministic Learning-Based Neural PID Control for Nonlinear Robotic Systems

Traditional proportional-integral-derivative(PID)controllers have achieved widespread success in industrial appli-cations.However,the nonlinearity and uncertainty of practical systems cannot be ignored,even though most of the existing research on PID controllers is focused on linear systems.There-fore,developing a PID controller with learning ability is of great significance for complex nonlinear systems.This article proposes a deterministic learning-based advanced PID controller for robot manipulator systems with uncertainties.The introduction of neu-ral networks(NNs)overcomes the upper limit of the traditional PID feedback mechanism's capability.The proposed control scheme not only guarantees system stability and tracking error convergence but also provides a simple way to choose the three parameters of PID by setting the proportional coefficients.Under the partial persistent excitation(PE)condition,the closed-loop system unknown dynamics of robot manipulator systems are accurately approximated by NNs.Based on the acquired knowl-edge from the stable control process,a learning PID controller is developed to further improve overall control performance,while overcoming the problem of repeated online weight updates.Sim-ulation studies and physical experiments demonstrate the validity and practicality of the proposed strategy discussed in this article.