Electromagnetic torque analysis‐based method for performance evaluation and optimisation of closed‐loop CPS regarding small signal stability

Sheng, Yibiao; Lin, Tao; Chen, Rusi; Chen, Baoping; Ding, Guili; Xu, Xialing; Xie, Chang; Liu, Lin

doi:10.1049/iet-cps.2018.5033

Cited by 3 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An embedded cyber-physical Mecanum vehicle system was developed by means of the proposed FPA-fuzzy EML technique. Figure 4 depicts the 5C architecture of the proposed metaheuristic cyber-physical system with five levels-Level 1: connection, Level 2: conversion, Level 3: cyber, Level 4: cognition, and Level 5: configuration [35][36][37]. This methodology integrates cyber computation and physical processes through sensors and actuators in a feedback loop.…”

Section: Embedded Cyber-physical Mecanum Robotic Systemmentioning

confidence: 99%

Evolutionary Machine Learning for Optimal Polar-Space Fuzzy Control of Cyber-Physical Mecanum Vehicles

Huang

2020

Electronics

View full text Add to dashboard Cite

This paper contributes to the development of evolutionary machine learning (EML) for optimal polar-space fuzzy control of cyber-physical Mecanum vehicles using the flower pollination algorithm (FPA). The metaheuristic FPA is utilized to design optimal fuzzy systems, called FPA-fuzzy. In this hybrid computation, both the fuzzy structure and the number of IF–THEN rules are optimized through the FPA evolutionary process. This approach overcomes the drawback of the structure tuning problem in conventional fuzzy systems. After deriving the polar-space kinematics model of Mecanum vehicles, an optimal EML FPA-fuzzy online control scheme is synthesized, and the global stability is proven via Lyapunov theory. An embedded cyber-physical robotic system is then constructed using the typical 5C strategy. The proposed FPA-fuzzy computation collaborates with the advanced sensors and actuators of Mecanum vehicles to design a cyber-physical robotic system. Compared with conventional Cartesian-space control methods, the proposed EML FPA-fuzzy has the advantages of metaheuristics, fuzzy online control, and cyber-physical system design in polar coordinates. Finally, the mechatronic design and experimental setup of a Mecanum vehicle cyber-physical system is constructed. Through experimental results and comparative works, the effectiveness and merit of the proposed methods are validated. The proposed EML FPA-fuzzy control approach has theoretical and practice significance in terms of its real-time capability, online parameter tuning, convergent behavior, and hybrid artificial intelligence.

show abstract

Section: Embedded Cyber-physical Mecanum Robotic Systemmentioning

confidence: 99%

Evolutionary Machine Learning for Optimal Polar-Space Fuzzy Control of Cyber-Physical Mecanum Vehicles

Huang

2020

Electronics

View full text Add to dashboard Cite

show abstract

A Comprehensive Study of Wide-Area Damping Controller Requirements Through Real-Time Evaluation with Operational Uncertainties in Modern Power Systems

Kumar¹,

Bhadu²

2022

IETE Journal of Research

View full text Add to dashboard Cite

A Networked Multirobot CPS With Artificial Immune Fuzzy Optimization for Distributed Formation Control of Embedded Mobile Robots

Huang

Kung

et al. 2020

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

Electromagnetic torque analysis‐based method for performance evaluation and optimisation of closed‐loop CPS regarding small signal stability

Cited by 3 publications

References 20 publications

Evolutionary Machine Learning for Optimal Polar-Space Fuzzy Control of Cyber-Physical Mecanum Vehicles

Evolutionary Machine Learning for Optimal Polar-Space Fuzzy Control of Cyber-Physical Mecanum Vehicles

A Comprehensive Study of Wide-Area Damping Controller Requirements Through Real-Time Evaluation with Operational Uncertainties in Modern Power Systems

A Networked Multirobot CPS With Artificial Immune Fuzzy Optimization for Distributed Formation Control of Embedded Mobile Robots

Contact Info

Product

Resources

About