Intelligent Thermal Control Strategy Based on Reinforcement Learning for Space Telescope

Xiong, Yan; Guo, Liang; Huang, Yong; Chen, Liheng

doi:10.2514/1.t5774

Cited by 13 publications

(6 citation statements)

References 22 publications

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“…In particular, CNN architectures have provided the state-of-the-art performance on core tasks within computer vision, computational neuroscience and medical image analysis [53], [54]. In addition, Deep Neural Network (DNN) architectures have found application in Natural Language Processing (NLP) for tasks such as learning word representations [55], [56], machine translation [57]- [59], language understanding [60], speech recognition [61], and advanced control systems [62].…”

Section: Deep Learning For Additive Manufacturing (Dlam) Methodsmentioning

confidence: 99%

DLAM: Deep Learning Based Real-Time Porosity Prediction for Additive Manufacturing Using Thermal Images of the Melt Pool

Zhang

Young

et al. 2021

IEEE Access

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This paper presents an investigation of the rapid variations in the temperature of metal melt pool for Additive Manufacturing (AM) processes. The melt pool is created by scanning a high-power laser beam across a metal powder bed. Rapid heating and cooling processes are involved in the layer-by-layer fabrication of the metal part. Recent advances in Machine Learning and Deep Learning algorithms provide efficient ways to analyze large sets of data in search of correlations that would otherwise be extremely time-consuming. The use of Machine Learning and Deep Learning algorithms to understand temperature variations in AM fabrication process will allow to predict the formation of porosity before it occurs. The objective of this research is to advance the AM technology using enhanced Deep Learning techniques to provide in-situ analysis of the melt pool temperature that can lead to a reliable manufacturing of Three-Dimensional (3D) metal parts/components. In specific, Deep Learning based porosity prediction for Additive Manufacturing (DLAM) methods have been proposed. In DLAMs, several state-of-the-art Deep Learning algorithms such as Convolutional Neural Networks (CNN) using transfer learning, and Residual-Recurrent Convolutional Neural Networks (Res-RCNN) are proposed for effectively performing the end-to-end porosity prediction in real-time using thermal images of melt pool. Experimental results, in this research, show that the Res-RCNN has an overall accuracy of 99.49% and inference time of 8.67ms, and the Res-RCNN outperforms other baseline models. The Res-RCNN's recursive architecture allows the network to view each input image multiples times and at varying feature levels, which enables a slight boost in porosity prediction accuracy over the commonly used transfer learning CNN models.

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Section: Deep Learning For Additive Manufacturing (Dlam) Methodsmentioning

confidence: 99%

DLAM: Deep Learning Based Real-Time Porosity Prediction for Additive Manufacturing Using Thermal Images of the Melt Pool

Zhang

Young

et al. 2021

IEEE Access

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“…AI techniques can also optimize the temperature control scheme of the telescope by analyzing the thermal characteristics of the telescope. 33,34,38 For example, the AI algorithms can determine the heat flow path inside the telescope by analyzing the temperature distribution of each component inside the telescope; it can also determine the heat dissipation effect of the telescope shell by analyzing the temperature change of the external environment of the telescope.…”

Section: Thermal Properties Analysismentioning

confidence: 99%

Challenges and application of artificial intelligence technology in the field of space optical telescopes

XIONG,

Liu,

Nan

et al. 2023

AOPC 2023: AI in Optics and Photonics

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“…The FO actor is used to learn the state-to-action mapping that generates the recommended FOPID controller parameters KðkÞ ¼ ½K P ðkÞ; K I ðkÞ; K D ðkÞ; nðkÞ; kðkÞ. The SAM unit is used to generate stochastically the actual FOPID controller parameters according to the recommended parameters suggested by the FO actor [54,74]. The FO critic receives the system state and external reinforcement signal (i.e., immediate reward r(k)) and produces a temporal difference (TD) error (i.e., d TD ðkÞ) and an estimated value function V(k) of the policy followed by the FO actor.…”

Section: The Proposed Fopid-foac Algorithmmentioning

confidence: 99%

“…Actor-critic learning algorithms have been a research hotspot in recent years because of their ability to learn and adapt to improve the performance of the controller [22,61]. To realize the critic and the actor, artificial neural networks (ANNs) were developed [17,54,74]. In [17], one ANN was used for the critic and another one for the actor.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Shalaby

El-Hossainy²,

Abozalam

et al. 2022

Neural Comput & Applic

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In this paper, an online optimization approach of a fractional-order PID controller based on a fractional-order actor-critic algorithm (FOPID-FOAC) is proposed. The proposed FOPID-FOAC scheme exploits the advantages of the FOPID controller and FOAC approaches to improve the performance of nonlinear systems. The proposed FOAC is built by developing a FO-based learning approach for the actor-critic neural network with adaptive learning rates. Moreover, a FO rectified linear unit (RLU) is introduced to enable the AC neural network to define and optimize its own activation function. By the means of the Lyapunov theorem, the convergence and the stability analysis of the proposed algorithm are investigated. The FO operators for the FOAC learning algorithm are obtained using the gray wolf optimization (GWO) algorithm. The effectiveness of the proposed approach is proven by extensive simulations based on the tracking problem of the two degrees of freedom (2-DOF) helicopter system and the stabilization issue of the inverted pendulum (IP) system. Moreover, the performance of the proposed algorithm is compared against optimized FOPID control approaches in different system conditions, namely when the system is subjected to parameter uncertainties and external disturbances. The performance comparison is conducted in terms of two types of performance indices, the error performance indices, and the time response performance indices. The first one includes the integral absolute error (IAE), and the integral squared error (ISE), whereas the second type involves the rising time, the maximum overshoot (Max. OS), and the settling time. The simulation results explicitly indicate the high effectiveness of the proposed FOPID-FOAC controller in terms of the two types of performance measurements under different scenarios compared with the other control algorithms.

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Intelligent Thermal Control Strategy Based on Reinforcement Learning for Space Telescope

Cited by 13 publications

References 22 publications

DLAM: Deep Learning Based Real-Time Porosity Prediction for Additive Manufacturing Using Thermal Images of the Melt Pool

DLAM: Deep Learning Based Real-Time Porosity Prediction for Additive Manufacturing Using Thermal Images of the Melt Pool

Challenges and application of artificial intelligence technology in the field of space optical telescopes

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

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