Self-Tuning PID Controller Using a Neural Network for Nonlinear Exoskeleton System

The article proposes the application of method analog neural networks (NN) with a radial basis function (RBF) and self-tuning of the proportional-integral derivative (PID) controller coefficients for a nonlinear control system of the lower extremities of the exoskeleton. The implementation of accurate and high-quality control of nonlinear systems, including parameter uncertainties and external disturbances, is possible using an analog neuron of a network controller, which has the ability to continuously learn and adapt. The NN in the PID controller allows us to correct errors that arise due to uncertainties and changes in parameters during the movement of the lower extremities of the exoskeleton. The effect of the proposed control algorithm is demonstrated by means of simulation in the Matlab/Simulink environment. © 2021 IEEE.