Work

Fuzzy Rules Data-Driven Equivalent Model with Multi-gradient Learning for Discrete-Time Nearly Optimal Control

Year: 2024

Type: article

Abstract: A behavior of switchable control directions is investigated for the non-holonomic robotic system considered as a class of unknown nonlinear discrete-time systems. The data-driven equivalent model is established by a multi-input fuzzy rule emulated network and the multi-gradient learning law is developed to tune all adjustable parameters. Thereafter, the nearly optimal controller is derived using the dynamics of the equivalent model and the closed-loop performance is analyzed through rigorous mathematical analysis. The experimental system is constructed to validate the effectiveness of the proposed scheme and the advantage of the multi-gradient approach.

Source: International Journal of Fuzzy Systems

DOI: 10.1007/s40815-024-01727-x

Author: C. Treesatayapun

Institutions: Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Walailak University

Language: English

Topic: Adaptive Dynamic Programming Control

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,690

Funder: Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional