ANFIS Based Reinforcement Learning Strategy for Control A Nonlinear Coupled Tanks System
| Autor: | Ali Hussien Mary, Mohammed Hussein Miry, Abbas H. Miry |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Adaptive neuro fuzzy inference system Computer science Inference system Control (management) 02 engineering and technology Nonlinear system 020901 industrial engineering & automation Control theory 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Markov decision process Electrical and Electronic Engineering |
| Zdroj: | Journal of Electrical Engineering & Technology. 17:1921-1929 |
| ISSN: | 2093-7423 1975-0102 |
| DOI: | 10.1007/s42835-021-00753-1 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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