ANFIS Based Reinforcement Learning Strategy for Control A Nonlinear Coupled Tanks System

被引：7
|
作者：
Mary, Ali Hussien [1 ]
Miry, Abbas H. [2 ]
Miry, Mohammed H. [3 ]
机构：
[1] Univ Baghdad, Al Khwarizmi Coll Engn, Mechatron Engn Dept, Baghdad, Iraq
[2] Mustansiriyah Univ, Dept Elect Engn, Baghdad, Iraq
[3] Univ Technol Baghdad, Dept Commun Engn, Baghdad, Iraq
来源：
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY | 2022年 / 17卷 / 03期
关键词：
ANFIS; Reinforcement learning; Nonlinear control; Level control; TRAJECTORY TRACKING; NEURAL-NETWORK;
D O I：
10.1007/s42835-021-00753-1
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, a novel algorithm based machine learning technique for control nonlinear coupled tanks system is presented. An intelligent controller using adaptive neuro-fuzzy inference system (ANFIS) based reinforcement learning is proposed (ANFIS-RL) by representing the nonlinear coupled tanks system as a Markov decision process. A model-free learning algorithm has been used to train a policy that controls the liquid level of the tanks system without the need to determine the dynamic model of the controlled system. Based on the optimal learned policy, which is approximated by ANFIS, the controlled system can perform the best action quickly based on the states of the system. Simulation results demonstrated the feasibility of the proposed algorithm.
引用
收藏
页码：1921 / 1929
页数：9
相关论文
共 50 条
  • [1] ANFIS Based Reinforcement Learning Strategy for Control A Nonlinear Coupled Tanks System
    Ali Hussien Mary
    Abbas H. Miry
    Mohammed H. Miry
    Journal of Electrical Engineering & Technology, 2022, 17 : 1921 - 1929
  • [2] Multivariable Coupled System Control Method Based on Deep Reinforcement Learning
    Xu, Jin
    Li, Han
    Zhang, Qingxin
    SENSORS, 2023, 23 (21)
  • [3] Research of Control Strategy of Power System Stabilizer Based on Reinforcement Learning
    Zhu, Xingyu
    Jin, Tao
    2020 IEEE 2ND INTERNATIONAL CONFERENCE ON CIRCUITS AND SYSTEMS (ICCS 2020), 2020, : 81 - 85
  • [4] A reinforcement learning-based transformed inverse model strategy for nonlinear process control
    Dutta, Debaprasad
    Upreti, Simant R.
    COMPUTERS & CHEMICAL ENGINEERING, 2023, 178
  • [5] Nonlinear system identification using ANFIS based on emotional learning
    Jalili-Kharaajoo, M
    ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2004, 2004, 3315 : 697 - 707
  • [6] Control Strategy of Microgrid Energy Storage System Based on Deep Reinforcement Learning
    Liang H.
    Li H.
    Zhang H.
    Hu Z.
    Qin Z.
    Cao J.
    Dianwang Jishu/Power System Technology, 2021, 45 (10): : 3869 - 3876
  • [7] Reinforcement Learning Behavioral Control for Nonlinear Autonomous System
    Zhenyi Zhang
    Zhibin Mo
    Yutao Chen
    Jie Huang
    IEEE/CAAJournalofAutomaticaSinica, 2022, 9 (09) : 1561 - 1573
  • [8] Reinforcement Learning Behavioral Control for Nonlinear Autonomous System
    Zhang, Zhenyi
    Mo, Zhibin
    Chen, Yutao
    Huang, Jie
    IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2022, 9 (09) : 1561 - 1573
  • [9] Nonlinear Control System with Reinforcement Learning and Neural Networks Based Lyapunov Functions
    Rego, Rosana Cibely Batista
    Araujo, Fabio Meneghetti Ugulino de
    IEEE LATIN AMERICA TRANSACTIONS, 2021, 19 (08) : 1253 - 1260
  • [10] Optimal synchronized control of nonlinear coupled harmonic oscillators based on actor–critic reinforcement learning
    Zhiyang Gu
    Chengli Fan
    Dengxiu Yu
    Zhen Wang
    Nonlinear Dynamics, 2023, 111 : 21051 - 21064
12345