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A REAL-TIME ADAPTIVE LEARNING CONTROL BASED ON EVOLUTIONARY ALGORITHM OF THE APPLICATION TO AN ELECTRO-HYDRAULIC SERVO SYSTEM

Sung Ouk Chang, Jin Kul Lee
2002 Proceedings of the JFPS International symposium on fluid power  
The mutation equation of evolutionary strategy uses the error that is generated from the dynamic system.  ...  These algorithms can be applied; the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics  ...  This thesis, to compose a real time adaptive learning controller according to the output of a dynamic system, applies an evolutionary algorithm to a feed forward neural network that is generally used by  ... 
doi:10.5739/isfp.2002.573 fatcat:4ueo3dwgbrcundo3rpf7oj5a7m

Data-driven Koopman operators for model-based shared control of human–machine systems

Alexander Broad, Ian Abraham, Todd Murphey, Brenna Argall
2020 The international journal of robotics research  
We present a data-driven shared control algorithm that can be used to improve a human operator's control of complex dynamic machines and achieve tasks that would otherwise be challenging, or impossible  ...  Our method assumes no a priori knowledge of the system dynamics.  ...  Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the aforementioned institutions.  ... 
doi:10.1177/0278364920921935 fatcat:ifxyziiub5g3dj7i5ijftx3toa

Machine Learning-Based Cognitive Position and Force Controls for Power-Assisted Human–Robot Collaborative Manipulation

S. M. Mizanoor Rahman
2021 Machines  
We also derived a novel adaptive control algorithm based on human characteristics. We experimentally evaluated those control methods and compared the system performance between the control methods.  ...  Again, the selection of appropriate control methods as well as inclusion of human factors in the controls is important to make the systems human friendly.  ...  The author acknowledges the support that he received from his past lab members and Ryojun Ikeura of Mie University, Japan.  ... 
doi:10.3390/machines9020028 fatcat:njkimcpirbdkra7p4n5rtxoknu

Special issue on extreme learning machine and deep learning networks

Zhihong Man, Guang-Bin Huang
2020 Neural computing & applications (Print)  
It is seen that before collecting the system input and output data and learning the systems dynamics, the noise is firstly used as the system control input.  ...  class of continuous-time Markov jump linear systems (MJLSs) with unknown system dynamics by using a parallel reinforcement learning (RL) algorithm.  ... 
doi:10.1007/s00521-020-05175-0 fatcat:4a6v6gptyzhy5ncwnmhuvhgwqq

Adaptive Human–Machine Evaluation Framework Using Stochastic Gradient Descent-Based Reinforcement Learning for Dynamic Competing Network

Jinbae Kim, Hyunsoo Lee
2020 Applied Sciences  
The rewards derived from the quantitative human evaluation are designed to be updated quickly and easily in an adaptive manner.  ...  Considering the special problem of reinforcement learning in an environment in which multiple network topologies coexist, we propose a policy that properly computes and updates the rewards derived from  ...  We designed an algorithm that learns faster by adaptively updating the reward value in the form of a real number derived from human evaluation.  ... 
doi:10.3390/app10072558 fatcat:q75624a7gbfofnluuwh2nxkuna

Structured Neural Network Dynamics for Model-based Control [article]

Alexander Broad, Ian Abraham, Todd Murphey, Brenna Argall
2018 arXiv   pre-print
The network is designed to mimic the properties of linear dynamical systems which makes analysis and control simple.  ...  The architecture facilitates the integration of learned system models with gradient-based model predictive control algorithms, and removes the requirement of computing potentially costly derivatives online  ...  An alternative option is to learn an explicit model of the system dynamics which can be incorporated into an optimal control algorithm.  ... 
arXiv:1808.01184v1 fatcat:mtxzyej7qvcqfcfmdabgahxhja

Improved Learning of Dynamics Models for Control [chapter]

Arun Venkatraman, Roberto Capobianco, Lerrel Pinto, Martial Hebert, Daniele Nardi, J. Andrew Bagnell
2017 Springer Proceedings in Advanced Robotics  
In this paper we present an extension to Data As Demonstrator for handling controlled dynamics in order to improve the multiple-step prediction capabilities of the learned dynamics models.  ...  Model-based reinforcement learning (MBRL) plays an important role in developing control strategies for robotic systems.  ...  Experimental Evaluation We evaluate our algorithm ('DAgger +DaD') both on simulated dynamical systems 2 and real robotic platforms.  ... 
doi:10.1007/978-3-319-50115-4_61 dblp:conf/iser/VenkatramanCPHN16 fatcat:iqvkht4kzjggbheblm6pmwy3ui

Contribution to the Control of a MAS's Global Behaviour: Reinforcement Learning Tools [chapter]

François Klein, Christine Bourjot, Vincent Chevrier
2009 Lecture Notes in Computer Science  
We propose an experimental dynamical approach to enhance the control of the global behaviour of a reactive multi-agent system.  ...  We use reinforcement learning tools to link global information of the system to control actions. We propose to use the behaviour of the system as this global information.  ...  A constraint of the RL algorithms used is that they learn a policy for a single target and they do not allow to change the target without learning a new policy from the beginning.  ... 
doi:10.1007/978-3-642-02562-4_10 fatcat:65o2sferhvfofpqvnzf6aq2ao4

Using Machine Learning Approach for Computational Substructure in Real-Time Hybrid Simulation [article]

Elif Ecem Bas, Mohamed A. Moustafa, David Feil-Seifer, Janelle Blankenburg
2020 arXiv   pre-print
Two different machine learning algorithms are evaluated to provide a valid and useful metamodeling solution for analytical substructure.  ...  RTHS test results using both LR and RNN models are evaluated, and the advantages and disadvantages of these models are discussed.  ...  Machine learning is the science of programming computers so that they can learn from data [14] .  ... 
arXiv:2004.02037v1 fatcat:5hfiah4jvzctnjq632qnecgmki

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Leandro L. S. Linhares, Aluisio I. R. Fontes, Allan M. Martins, Fábio M. U. Araújo, Luiz F. Q. Silveira
2015 Mathematical Problems in Engineering  
Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs) are an efficient tool to identify nonlinear systems.  ...  The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise.  ...  is used to identify a real dynamical system, considering that its experimental data is corrupted by noise and outliers.  ... 
doi:10.1155/2015/678965 fatcat:fivbswqxivcwhhokytwfex3qcm

Memetic robot control evolution and adaption to reality

Else-Line Ruud, Eivind Samuelsen, Kyrre Glette
2016 2016 IEEE Symposium Series on Computational Intelligence (SSCI)  
This paper explores the effects of learning combined with a genetic algorithm to evolve control system parameters for a four-legged robot.  ...  On the direct results from evolution in simulation, Lamarckian learning showed promising results, with a significant increase in final fitness compared with the results from evolution without learning.  ...  A description of the experimental setup is given in the first subsection, before the results from evolution of the robot control system parameters is presented in Subsection III-B. A.  ... 
doi:10.1109/ssci.2016.7850169 dblp:conf/ssci/RuudSG16 fatcat:br73isxrrved7j65smhm4yytne

Deep Koopman Operator with Control for Nonlinear Systems [article]

Haojie Shi, Max Q.-H. Meng
2022 arXiv   pre-print
Experimental results demonstrate that our approach outperforms other existing methods, reducing the prediction error by order of magnitude and achieving superior control performance in several nonlinear  ...  Furthermore, most Koopman-based algorithms only consider nonlinear systems with linear control input, resulting in lousy prediction and control performance when the system is fully nonlinear with the control  ...  Experimental results prove that this approach is also conducive to the control of general systems with high nonlinearity on control terms.  ... 
arXiv:2202.08004v2 fatcat:eapumwddnzgmzk2iw7s6ne63xe

Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control [article]

JunPing Wang, WenSheng Zhang, Ian Thomas, ShiHui Duan, YouKang Shi
2018 arXiv   pre-print
Results on three groups of discrete-time nonlinear control tasks show that our proposed model can availably improve the performance of task with the help of other related tasks.  ...  all of tasks, and online evaluate performance of system actions in discrete-time nonlinear systems.  ...  ACKNOWLEDGMENT The authors also would like to thank anonymous editor and reviewers who gave valuable suggestion that has helped to improve the quality of the manuscript.  ... 
arXiv:1807.00298v1 fatcat:we2c24rbnrfkbeanpjdqihxljq

Research on Discrete Dynamic Modeling of Learner Behavior Analysis in English Teaching

Junru Fu, Lingmei Cao, Le Sun
2022 Computational Intelligence and Neuroscience  
Secondly, combined with English teaching content and teaching objectives, through the analysis of various data of students' learning behavior, this paper evaluates students' English teaching quality from  ...  The results show that compared with the current innovative English teaching methods based on a dynamic iterative decision algorithm, the personalized discrete dynamic English teaching model based on learner  ...  proposed that when designing a feedback controller for modeling discrete dynamic systems, the time-varying perturbation of controller parameters may lead to the performance degradation of closed-loop systems  ... 
doi:10.1155/2022/1914996 pmid:35720913 pmcid:PMC9203174 fatcat:a3fqpszunzgxngk4zybj3yst5a

Decentralized Multi-tasks Distribution in Heterogeneous Robot Teams by Means of Ant Colony Optimization and Learning Automata [chapter]

Javier de Lope, Darío Maravall, Yadira Quiñonez
2012 Lecture Notes in Computer Science  
We have evaluated the robustness of the algorithm, perturbing the number of pending loads to simulate the robot's error in estimating the real number of pending tasks and also the dynamic generation of  ...  The paper ends with a critical discussion of experimental results.  ...  Experimental Results We have carried out a series of experiments to evaluate the system performance index by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based  ... 
doi:10.1007/978-3-642-28942-2_10 fatcat:hl27hpev2zg53dubwy7ujjbtaq
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