Work

Neural Network Functional Observer‐Based Composite Anti‐Disturbance Control for Systems With Multiplicative and Implicit Disturbances

Year: 2026

Type: article

Abstract: ABSTRACT High precision control under disturbances and uncertainties is critical to the safe, stable, and long‐term continuous operation of the system. In this paper, a composite anti‐disturbance control strategy for systems affected by both multiplicative and implicit disturbances is proposed. First, a reduced neural network functional observer is developed to estimate the partially unknown states and the implicit disturbances, which takes into account the effect of the multiplicative disturbances and the uncertainties in the implicit disturbances, both of which are related to the system's states. The neural network is employed for approximating the multiplicative disturbances. Then, a dynamic sliding mode surface with disturbance compensation is introduced to ensure exponential convergence of systems with the multiplicative disturbances and the implicit disturbances. Furthermore, a novel barrier function‐based adaptive sliding mode control law is designed to guarantee that the system trajectories reach the sliding surface, which greatly reduces chattering without requiring prior knowledge of the upper bound on system uncertainties. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach.

Source: International Journal of Robust and Nonlinear Control

DOI: 10.1002/rnc.70334

Authors: Baopeng Zhu, Yu Wang, Yangyang Cui, Yukai Zhu

Institutions: Southeast University, Beihang University

Language: English

Topic: Adaptive Control of Nonlinear Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Beijing Municipal Natural Science Foundation, Fundamental Research Funds for the Central Universities