GWO-based fuzzy modeling for nonlinear composite systems

ZY Chen , Yahui Meng , Ruei-Yuan Wang , Timothy Chen

Abstract

The goal of this work is to create a new and improved GWO (Grey Wolf Optimizer), the so-called Robot GWO (RGWO), for dynamic and static target tracking involving multiple robots in unknown environmental conditions. From applying ourselves with the Gray Wolf Optimization Algorithm (GWO) and how it works, as the name suggests, it is a nature-inspired metaheuristic based on the behavior of wolf packs. Like other nature-inspired metaheuristics such as genetic algorithms and firefly algorithms, we explore the search space to find the optimal solution. The results also show that the improved optimal control method can provide superior power characteristics even when operating conditions and design parameters are changed.

Key Words

feedback and feedforward; fuzzy LMI control; improved optimal control performance; linearization method

Address

ZY Chen and Ruei-Yuan Wang — School of Science, Guangdong University of Petrochemical Technology, Maoming 525000, Guangdong, China
Yahui Meng — 1)School of Science, Guangdong University of Petrochemical Technology, Maoming 525000, Guangdong, China 2)InterNetworks Research Laboratory School of Computing (SOC) University Utara Malaysia 06010 UUM Sintok, Malaysia
Timothy Chen — California Institute of Technology, Pasadena, CA 91125, USA

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