• 제목/요약/키워드: robust learning

검색결과 550건 처리시간 0.024초

Dynamic control of mobile robots using a robust.adaptive learning control method (강인.적응학습제어 방식에 의한 이동로봇의 동력학 제어)

  • Nam, Jae-Ho;Baek, Seung-Min;Guk, Tae-Yong
    • Journal of Institute of Control, Robotics and Systems
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    • 제4권2호
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    • pp.178-186
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    • 1998
  • In this paper, a robust.adaptive learning control scheme is presented for precise trajectory tracking of rigid mobile robots. In the proposed controller, a set of desired trajectories is defined and used in constructing the control input and learning rules which constitute the main part of the proposed controller. Stable operating characteristics such as precise trajectory tracking, parameter estimation, disturbance suppression, etc., are shown thorugh experiments and computer simulations.

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Robust Iterative Learning Control Alorithm

  • Kim, Yong-Tae;Zeungnam Bien
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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    • pp.71-77
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    • 1995
  • In this paper are proposed robust iterative learning control(ILC) algorithms for both linear continuous time-invariant system and linear discrete-time system. In contrast to conventional methods, the proposed learning algorithms are constructed based on both time domain performance and iteration-domain performance. The convergence of the proposed learning algorithms is proved. Also, it is shown that the proposed method has robustness in the presence of external disturbances and the convergence accuracy can be improved. A numerical external disturbances and the convergence accuracy can be improved. A numerical example is provided to show the effectiveness of the proposed algorithm.

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Robust appearance feature learning using pixel-wise discrimination for visual tracking

  • Kim, Minji;Kim, Sungchan
    • ETRI Journal
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    • 제41권4호
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    • pp.483-493
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    • 2019
  • Considering the high dimensions of video sequences, it is often challenging to acquire a sufficient dataset to train the tracking models. From this perspective, we propose to revisit the idea of hand-crafted feature learning to avoid such a requirement from a dataset. The proposed tracking approach is composed of two phases, detection and tracking, according to how severely the appearance of a target changes. The detection phase addresses severe and rapid variations by learning a new appearance model that classifies the pixels into foreground (or target) and background. We further combine the raw pixel features of the color intensity and spatial location with convolutional feature activations for robust target representation. The tracking phase tracks a target by searching for frame regions where the best pixel-level agreement to the model learned from the detection phase is achieved. Our two-phase approach results in efficient and accurate tracking, outperforming recent methods in various challenging cases of target appearance changes.

Open set Object Detection combining Multi-branch Tree and ASSL (다중 분기 트리와 ASSL을 결합한 오픈 셋 물체 검출)

  • Shin, Dong-Kyun;Ahmed, Minhaz Uddin;Kim, JinWoo;Rhee, Phill-Kyu
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • 제18권5호
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    • pp.171-177
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    • 2018
  • Recently there are many image datasets which has variety of data class and point to extract general features. But in order to this variety data class and point, deep learning model trained this dataset has not good performance in heterogeneous data feature local area. In this paper, we propose the structure which use sub-category and openset object detection methods to train more robust model, named multi-branch tree using ASSL. By using this structure, we can have more robust object detection deep learning model in heterogeneous data feature environment.

Feedback-Based Iterative Learning Control for MIMO LTI Systems

  • Doh, Tae-Yong;Ryoo, Jung-Rae
    • International Journal of Control, Automation, and Systems
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    • 제6권2호
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    • pp.269-277
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    • 2008
  • This paper proposes a necessary and sufficient condition of convergence in the $L_2$-norm sense for a feedback-based iterative learning control (ILC) system including a multi-input multi-output (MIMO) linear time-invariant (LTI) plant. It is shown that the convergence conditions for a nominal plant and an uncertain plant are equal to the nominal performance condition and the robust performance condition in the feedback control theory, respectively. Moreover, no additional effort is required to design an iterative learning controller because the performance weighting matrix is used as an iterative learning controller. By proving that the least upper bound of the $L_2$-norm of the remaining tracking error is less than that of the initial tracking error, this paper shows that the iterative learning controller combined with the feedback controller is more effective to reduce the tracking error than only the feedback controller. The validity of the proposed method is verified through computer simulations.

Gain Tuning for SMCSPO of Robot Arm with Q-Learning (Q-Learning을 사용한 로봇팔의 SMCSPO 게인 튜닝)

  • Lee, JinHyeok;Kim, JaeHyung;Lee, MinCheol
    • The Journal of Korea Robotics Society
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    • 제17권2호
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    • pp.221-229
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    • 2022
  • Sliding mode control (SMC) is a robust control method to control a robot arm with nonlinear properties. A high switching gain of SMC causes chattering problems, although the SMC allows the adequate control performance by giving high switching gain, without the exact robot model containing nonlinear and uncertainty terms. In order to solve this problem, SMC with sliding perturbation observer (SMCSPO) has been researched, where the method can reduce the chattering by compensating the perturbation, which is estimated by the observer, and then choosing a lower switching control gain of SMC. However, optimal gain tuning is necessary to get a better tracking performance and reducing a chattering. This paper proposes a method that the Q-learning automatically tunes the control gains of SMCSPO with an iterative operation. In this tuning method, the rewards of reinforcement learning (RL) are set minus tracking errors of states, and the action of RL is a change of control gain to maximize rewards whenever the iteration number of movements increases. The simple motion test for a 7-DOF robot arm was simulated in MATLAB program to prove this RL tuning algorithm. The simulation showed that this method can automatically tune the control gains for SMCSPO.

Echo Noise Robust HMM Learning Model using Average Estimator LMS Algorithm (평균 예측 LMS 알고리즘을 이용한 반향 잡음에 강인한 HMM 학습 모델)

  • Ahn, Chan-Shik;Oh, Sang-Yeob
    • Journal of Digital Convergence
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    • 제10권10호
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    • pp.277-282
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    • 2012
  • The speech recognition system can not quickly adapt to varied environmental noise factors that degrade the performance of recognition. In this paper, the echo noise robust HMM learning model using average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise HMM learning model consists of the recognition performance is evaluated. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 3.1dB, recognition rate improved as 3.9%.

Design of DNP Controller for Robust Control Auto-Systems (DNP에 의한 자동화 시스템의 강인제어기 설계)

  • 김종옥;조용민;민병조;송용화;조현섭
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 한국조명전기설비학회 1999년도 학술대회논문집-국제 전기방전 및 플라즈마 심포지엄 Proceedings of 1999 KIIEE Annual Conference-International Symposium of Electrical Discharge and Plasma
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    • pp.121-126
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    • 1999
  • In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. In order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment systems is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

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Design of DNP Controller for Robust Control of Auto-Equipment Systems (자동화 설비시스템의 강인제어를 위한 DNP 제어기 설계)

  • ;趙賢燮
    • The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • 제13권2호
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    • pp.187-187
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    • 1999
  • in order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment system is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulation are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

Optimal deep machine learning framework for vibration mitigation of seismically-excited uncertain building structures

  • Afshin Bahrami Rad;Javad Katebi;Saman Yaghmaei-Sabegh
    • Structural Engineering and Mechanics
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    • 제88권6호
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    • pp.535-549
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    • 2023
  • Deep extreme learning machine (DELM) and multi-verse optimization algorithms (MVO) are hybridized for designing an optimal and adaptive control framework for uncertain buildings. In this approach, first, a robust model predictive control (RMPC) scheme is developed to handle the problem uncertainty. The optimality and adaptivity of the proposed controller are provided by the optimal determination of the tunning weights of the linear programming (LP) cost function for clustered external loads using the MVO. The final control policy is achieved by collecting the clustered data and training them by DELM. The efficiency of the introduced control scheme is demonstrated by the numerical simulation of a ten-story benchmark building subjected to earthquake excitations. The results represent the capability of the proposed framework compared to robust MPC (RMPC), conventional MPC (CMPC), and conventional DELM algorithms in structural motion control.