Q-Learning-based parameters adaptive algorithm for active disturbance rejection control and its application to ship course control

•First, we design a LADRC ship course controller to deal with nonlinearity and uncertainty caused by complex sea conditions, such as wind, waves and load.•Second, to achieve better control performance, we propose an adaptive algorithm based on Q-learning.•Furthermore, some desired states are initial...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 408; pp. 51 - 63
Main Authors: Chen, Zengqiang, Qin, Beibei, Sun, Mingwei, Sun, Qinglin
Format: Journal Article
Language:English
Published: Elsevier B.V 30.09.2020
Subjects:
Active disturbance rejection control (ADRC)
Adaptive control
Q-Learning
Reinforcement learning
Ship course control
Active disturbance rejection control (ADRC)
Q-Learning
Adaptive control
Reinforcement learning
Ship course control
ISSN:0925-2312, 1872-8286
Online Access:Get full text
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Summary:•First, we design a LADRC ship course controller to deal with nonlinearity and uncertainty caused by complex sea conditions, such as wind, waves and load.•Second, to achieve better control performance, we propose an adaptive algorithm based on Q-learning.•Furthermore, some desired states are initialized with higher probability during the training in order to train the corresponding Q-value more adequately. This paper concerns with the method of parameters tuning and the capability of active disturbance rejection control (ADRC) to the nonlinear plants. Firstly, an adaptive method of ADRC parameters based on Q-learning is proposed. Besides, to verify the effectiveness of the proposed parameter tuning strategy, the novel method is applied to the ship course control which has multifarious uncertainties due to the disturbance of wind, waves and currents. And then, for better control performance, when the training of Q value-table, the states stochastic initialize of each episode is not equiprobability, different state has different weights. The simulation results of both adaptive ADRC and linear active disturbance rejection control (LADRC) show that the proposed algorithm has the advantages of robustness and higher tracking precision.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2019.10.060