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10th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Saadatmand, Sepehr Kavousi, Mohammadamir Azizi, Sima Missouri Univ S&T Dept Elect & Comp Engn Rolla MO 65409 USA Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

ISBN: (纸本)9781728137834

In this paper, a dual heuristic programming controller is proposed to control a boost converter. Conventional controllers such as proportional-integral-derivative (PID) or proportional-integral (PI) are designed based on the linearized small-signal model near the operating point. Therefore, the performance of the controller during start-up, load change, or input voltage variation is not optimal since the system model changes by varying the operating point. The dual heuristic programming controller optimally controls the boost converter by following the approximate dynamic programming. The advantage of the DHP is that the neural network-based characteristic of the proposed controller enables boost converters to easily cope with large disturbances. A DHP with a well-trained critic and action networks can perform as an optimal controller for the boost converter. To compare the effectiveness of the traditional PI-based and the DHP boost converter, the simulation results are provided.

关键词： adaptive critic design Boost converter DC-DC converters Model predictive controller Dual heuristic programming reinforcement learning

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Model-free Based reinforcement learning Control Strategy of Aircraft Attitude Systems

Model-free Based Reinforcement Learning Control Strategy of ...

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Chinese Automation Congress (CAC)

作者： Huang, Dingcui Hu, Jiangping Peng, Zhinan Chen, Bo Hao, Mingrui Ghosh, Bijoy Kumar Univ Elect Sci & Technol China Sch Automat Engn Chengdu Peoples R China Syst Control & Intelligent Agent Cooperat Lab Sci & Technol Complex Beijing Peoples R China Texas Tech Univ Dept Math & Stat Lubbock TX 79409 USA

ISBN: (纸本)9781728176871

Traditional aircraft control algorithms have a strong dependence on system models, and are difficult to cope with the increasingly complex battlefield environment for intelligent aircrafts. In this paper, a model-free reinforcement learning is proposed to solve an attitude stabilization problem of an aircraft based online intelligent control strateu. The attitude control problem is firstly formulated as an optimal control problem, and then an adaptive dynamic programming (ADP) technology is applied to compute the corresponding nonlinear Hamilton-Jacobi-Bellman (HJB) equation. Then, an actor-critic neural network structure is established to learn the optimal controller online not requiring the information of the aircraft dynamics. The proposed intelligent control strategy enables the aircraft to adjust its attitude according to the actual mission targets and environments under the proposed online control strategy, so that autonomous learning and intelligent operation can be realized. Finally, simulation examples are presented to validate the proposed model-free based control strategy.

关键词： Model-Free Control reinforcement learning Aircraft Attitude System Actor-Critic Neural Network

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Distributed Frequency Controller for MT-HVDC Systems Via adaptive dynamic programming 32

Distributed Frequency Controller for MT-HVDC Systems Via Ada...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Hu, Zhongjie Liu, Zhi-Wei Hu, Xiong Chi, Ming Huazhong Univ Sci & Technol Coll Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9781728158556

The exact mathematical models of multi-terminal high-voltage direct current (MT-HVDC) systems are hard to be obtained in the practical application of HVDC technology. To overcome this challenge, a model-free distributed frequency controller for MT-HVDC systems is proposed based on adaptive dynamic programming (ADP) in this paper. Specifically, for each AC area, only the local and neighboring sampling data of frequency is required without the MT-HVDC system model. Besides, the proposed controller is distributed which effectively balances the communication burden among the AC areas. Moreover, the proposed controller makes the connected AC areas share their power reserves via HVDC grids to compensate load disturbances so that the necessary power reserves can be downsized. Some simulations carried out on a five-terminal HVDC system evaluate the performance of the proposed controller.

关键词： MT-HVDC system distributed frequency controller adaptive dynamic programming (ADP) neural network reinforcement learning

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A Time-Varying Deep reinforcement Model Predictive Control for DC Power Converter Systems

A Time-Varying Deep Reinforcement Model Predictive Control f...

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ieee International symposium on Power Electronics for Distributed Generation Systems (PEDG)

作者： Milad Andalibi Mojtaba Hajihosseini Sam Teymoori Maryam Kargar Meysam Gheisarnejad Shiraz University Shiraz Iran Aarhus University Aarhus Denmark UCSC Santa Cruz California United States Islamic Azad University Tehran Iran Islamic Azad University Najafabad Iran

Today power converters, especially DC/DC converters, is of great importance in power electronics applications such as DC micro-grids (MGs). However, they have some limitation such as inability to handle constant power load (CPL) which results in instability problems in MGs. Thus, a controller with specific characters including, robustness and fast response to system dynamic is vital to address the unsteadiness. In this paper, an adaptive model prediction controller (AMPC) based on Deep reinforcement learning (DRL) is developed to tackle the de-stabilization problem. In the proposed AMPC controller, the controlling signal coefficient in each variable operation point is regarded as the adjustable controller parameter and adaptively designed by the learning ability of the Deep Q- Network (DQN) strategy, leading to a robust controlling approach. We have shown that our suggested smart controller for DC/DC converters feeding CPLs is robust and fast in dynamic response.

关键词： Adaptation models System dynamics reinforcement learning Predictive models Power electronics Robustness Distributed power generation

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On closed-loop stability of model predictive controllers with learning costs 18

On closed-loop stability of model predictive controllers wit...

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18th European Control Conference (ECC)

作者： Beckenbach, Lukas Osinenko, Pavel Streif, Stefan Tech Univ Chemnitz Automat Control & Syst Dynam Lab D-09107 Chemnitz Germany

ISBN: (纸本)9783907144022

Model predictive controllers are commonly associated with a fixed running and/or terminal cost function. Recently, some possibilities of cost function adaptation inspired by reinforcement learning were investigated. The current study analyzes closed-loop stability of such controllers in a general way. It is shown what constraints on learned running and terminal cost are required for this sake. A particular feature of the suggested control scheme is that, unlike in some common model predictive controllers, an assumed local Lyapunov function does not have to satisfy a decay function not less than the running cost. Relation of the considered control scheme to a baseline model predictive controller and adaptive dynamic programming is discussed. In a case study, it is shown how different cost function adaptation schemes lead to different performance with respect to the infinite-horizon cost.

关键词： Cost functions

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Autonomous learning with Automatically Created Models and a Novel Model Selection

Autonomous Learning with Automatically Created Models and a ...

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ieee symposium Series on Computational Intelligence (SSCI)

作者： Harshal V Bharatia ACM Student Member Plano USA

ISBN: (纸本)9781728190495

An autonomous learning approach is presented here for expansive problem domains that may undergo frequent changes. It is hard to train and adapt learning-models to changes when the problem domain is very large. With the autonomous learning approach, a system performs knowledge refinement automatically by determining what can be improved and trains itself without explicit guidance to do so. It uses novel techniques to automatically create the learning-models and select an optimal model for each prediction. It automatically builds dynamic ensembles of models that incorporate specific improvisations, such as improvements based on past knowledge or specific trends reflected in the results, and overshadows sub-optimal portions of existing model. A reinforcement learning based model selector identifies which model is optimal for handling a request using a novel approach with automatically created hierarchical states. As a result, smart adaptive solutions that go beyond initial training become possible. This also alleviates the need for big complex monolithic models that require extensive training, automatically adapts to changes in the domain and offers better control over its performance. Experimental results show this approach learns quickly, adapts to changes very rapidly and performs quite well against prevalent learning methods.

关键词： Training learning systems Adaptation models Computational modeling reinforcement learning Switches Predictive models

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adaptive Optimal Output Regulation of Time-Delay Systems via Measurement Feedback

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2019年第3期30卷 938-945页

作者： Gao, Weinan Jiang, Zhong-Ping Georgia Southern Univ Dept Elect & Comp Engn Allen E Paulson Coll Engn & Comp Statesboro GA 30460 USA NYU Dept Elect & Comp Engn Tandon Sch Engn Brooklyn NY 11201 USA

This brief proposes a novel solution to problems related to the measurement feedback adaptive optimal output regulation of discrete-time linear systems with input time-delay. Based on reinforcement learning and adaptive dynamic programming, an approximate optimal control policy is obtained via recursive numerical algorithms using online information. Convergence proofs for the proposed algorithms are given. Notably, the exact knowledge of the plant and the exosystem is not needed. The learned control policy is only a function of retrospective input and measurement output data. Theoretical analysis and an application to a grid-connected inverter show that the proposed methodologies serve as effective tools for solving adaptive and optimal output regulation problems.

关键词： Measurement feedback control optimal control output regulation reinforcement learning time-delay systems

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An adaptive Model Management Strategy: Balancing Exploration and Exploitation

An Adaptive Model Management Strategy: Balancing Exploration...

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ieee symposium Series on Computational Intelligence (SSCI)

作者： Caie Hu Sanyou Zeng Changhe Li Mechanical Engineering and Electronic Information China University of Geoscience Wuhan China Automation and Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geoscience Wuhan China

ISBN: (纸本)9781728190495

Model management is a critical component in data-driven surrogate-assisted evolutionary optimization. It is mainly used for selecting candidate samples to be evaluated using exact functions to balance the exploration and exploitation of algorithms. However, there are some potential limitations for typical infill sampling criteria. An adaptive model management strategy is proposed to overcome the limitations. It adaptively adjusts the selection probability of candidate samples, and avoids potentially unreliability and the burden of selecting parameters of typical infill sampling criteria. The comparison results on benchmark problems demonstrate the competitiveness of the strategy in solving data-driven optimization problems.

关键词： Adaptation models Toy manufacturing industry reinforcement learning Evolutionary computation Benchmark testing Linear programming Optimization

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Resource Provisioning in Fog Computing through Deep reinforcement learning

Resource Provisioning in Fog Computing through Deep Reinforc...

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IFIP/ieee International symposium on Integrated Network Management

作者： José Santos Tim Wauters Bruno Volckaert Filip De Turck IDLab Ghent University - imec Gent Belgium

The massive growth of connected devices has made traditional cloud systems inadequate to sustain the scalability, mobility, and heterogeneous nature of the Internet of Things (oT). Distributed clouds have become a potential business opportunity for many service providers enabling the deployment of services on computational resources from the cloud up to the edge. However, challenges persist in fog-cloud infrastructures. One of them is known as Service Function Chaining (SFC), where providers benefit from network softwarization to create virtual chains of connected micro-services. Research has tackled SFC Allocation (SFCA) through theoretical modeling and heuristic algorithms, which often cannot cope with the dynamic behavior of the network. Recent works have addressed these challenges through Machine learning (ML), which can be capable of dynamically reconfiguring cloud-native service requirements over the continuum of virtual resources in next-generation networks. Thus, in this paper, a Deep reinforcement learning (DRL) approach is proposed for SFCA in Fog Computing focused on energy efficiency. Our agent learns about the best resource allocation decisions, focused on reducing costs from a previously presented Mixed-integer linear programming (MILP) formulation. Results show that our agent achieves comparable performance to state-of-the-art MILP formulations during dynamic use cases, obtaining 95% of request acceptance.

关键词： Training Radio frequency Cloud computing Service function chaining Heuristic algorithms Computational modeling Scalability

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ISBI 2020 - 2020 ieee International symposium on Biomedical Imaging

ISBI 2020 - 2020 IEEE International Symposium on Biomedical ...

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17th ieee International symposium on Biomedical Imaging, ISBI 2020

ISBN: (纸本)9781538693308

The proceedings contain 424 papers. The topics discussed include: segmentation-based method combined with dynamic programming for brain midline delineation;automatic extraction and sign determination of respiratory signal in real-time cardiac magnetic resonance imaging;diffeomorphic smoothing for retinotopic mapping;deepseed: 3D squeeze-and-excitation encoder-decoder convolutional neural networks for pulmonary nodule detection;deblurring cataract surgery videos using a multi-scale deconvolutional neural network;automated hemorrhage detection from coarsely annotated fundus images in diabetic retinopathy;open-set OCT image recognition with synthetic learning;temporally adaptive-dynamic sparse network for modeling disease progression;and bone structures extraction and enhancement in chest radiographs via CNN trained on synthetic data.

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