Work

Design of composite adaptive controller with multilateral adaptive learning mechanism

Year: 2025

Type: article

Abstract: To enhance trajectory tracking performance for affine nonlinear systems with parametric uncertainties and improve parameter convergence under interval excitation, this paper proposes a multilateral cooperative adaptive learning mechanism. The initial parameter values are assigned based on available data distribution or predefined bounds when unknown. A composite learning adaptive controller estimates system uncertainties using multilateral learning outputs. Adaptive update laws for unknown parameters and multilateral weights are designed using parameter estimation errors and approximation errors, with a saturation function constraining weight variation rates to suppress oscillations. Experimental results on an inverted pendulum system demonstrate the superiority of the proposed controller over two conventional adaptive controllers.

Source: Scientific Reports

DOI: 10.1038/s41598-025-26366-6

Authors: Chao Niu, Yumei Yao, Zengliang Zhang, Mingxin Zuo

Institutions: Henan Mechanical and Electrical Vocational College, Henan Polytechnic University

Language: English

Topic: Adaptive Control of Nonlinear Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funder: Henan Polytechnic University