RBFNN-Based ADRC Design for Continuous-Time Systems with Unknown Nonlinear Dynamics Subject to Time-Varying Disturbance

• Published in: 2023 42nd Chinese Control Conference (CCC) pp. 2610 - 2615
• Main Authors: Yan, Shuai, Hao, Shoulin, Yu, Haichen, Liu, Tao, Yan, Bin, Gong, Yihui
• Format: Conference Proceeding
• Language: English
• Published: Technical Committee on Control Theory, Chinese Association of Automation 24.07.2023
• Subjects: Active disturbance rejection control; Estimation error; Feedback control; Nonlinear dynamical systems; nonlinear dynamics; Observers; Radial basis function networks; RBFNN-based ESO; Robust control; time-varying disturbance; Uncertainty
• ISSN: 2161-2927
• DOI: 10.23919/CCC58697.2023.10240444

In this paper, a radial basis function neural network (RBFNN) based active disturbance rejection control (ADRC) scheme is proposed for continuous-time systems with unknown nonlinear dynamics and time-varying disturbance. By using RBFNN to online approximate the unknown nonlinear dynamics, a novel nonlinear extended state observer (ESO) is firstly designed to estimate the total disturbance consisting of time-varying external disturbance and RBFNN approximation error. Then, an anti-disturbance feedback control law together with a tracking differentiator is designed to counteract the total disturbance in a feedforward manner. The bounded convergence of the closed-loop system and ESO as well as the estimation errors of the weighting vector are rigorously analyzed based on the Lyapunov stability theory. A case study is carried out to demonstrate the effectiveness and merit of the proposed design, in contrast to the conventional ADRC.