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Control of an AUV with completely unknown dynamics and multi-asymmetric input constraints via off-policy reinforcement learning
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Control of an AUV with completely unknown dynamics and multi-asymmetric input constraints via off-policy reinforcement learning
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Control of an AUV with completely unknown dynamics and multi-asymmetric input constraints via off-policy reinforcement learning
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Journal Article
Control of an AUV with completely unknown dynamics and multi-asymmetric input constraints via off-policy reinforcement learning
2022
Overview
This paper investigates a novel model-free optimal controller for nonlinear autonomous underwater vehicles (AUVs). It is considered that the AUV considered as the case study is subject to multi-asymmetric constrained inputs. To achieve the optimal controller, a performance index function with exponential discounted value term and input hyperbolic function is developed. Since it is assumed that the AUV dynamics are completely unknown, a model-free integral reinforcement learning (RL) strategy is established. The suggested approach uses the sampled data pairs of input and states. To implement the model-free Integral RL optimal controller, a neural network structure is suggested to estimate the performance index function and control policy. Finally, a numerical simulation and comparative results are given to verify the effectiveness of the proposal.
Publisher
Springer London,Springer Nature B.V
Subject
/ Autonomous underwater vehicles
/ Computational Biology/Bioinformatics
/ Computational Science and Engineering
/ Data Mining and Knowledge Discovery
/ Image Processing and Computer Vision
/ Learning
/ Probability and Statistics in Computer Science
/ S.I.: Computational Intelligence-based Control and Estimation in Mechatronic System
/ Special Issue on Computational Intelligence-based Control and Estimation in Mechatronic Systems
