http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Nguyen Trong Tai (검색결과 7 건)
- 무료
- 기관 내 무료
- 유료
-
Apply Adaptive Fuzzy Sliding Mode Control to SMA Actuator
Nguyen Trong Tai,Kyoung Kwan Ahn 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10
In this paper, an adaptive fuzzy sliding-mode controller (AFSMC) is proposed to control SMA actuator. An approach of self-tuning fuzzy sliding-mode control which combines fuzzy control with the sliding-mode control (SMC), is presented for the tracking control of a class of nonlinear systems with the parameter uncertainties. Based on Lyapunov theory, the asymptotic stability of the overall systems is proved. The controller is applied to compensate the hysteresis phenomenon of SMA. The control results show that the controller is applied successfully to SMA.
-
Output Feedback Direct Adaptive Controller for a SMA Actuator With a Kalman Filter
Nguyen Trong Tai,Kyoung Kwan Ahn IEEE 2012 IEEE transactions on control systems technology Vol.20 No.4
<P>In this brief, a direct adaptive controller (DAC) is proposed to control a shape memory alloy (SMA) actuator. The DAC, with its advantages in parameter tuning and noise robustness, was successfully applied to control an SMA actuator. The control signal in DAC was derived via a feedback linearization method. A radial basis function neural network (RBFNN) was then employed to approximate the control signal due to the system nonlinearity and parameter uncertainties. The weighting factors of the RBFNN are updated on the condition of system stability. Due to the system states requirement of the DAC and measurement noise, a Kalman filter was introduced in this work to eliminate the output measurement noise and estimate the system states. From the simulation and experimental results, it was verified that the DAC controller with the Kalman filter was successfully applied to a SMA actuator and the hysteresis phenomenon was almost compensated. The experimental control results were also compared with those of a conventional proportional-integral-derivative controller.</P>
-
A RBF Neural Network Sliding Mode Controller for SMA Actuator
Tai, Nguyen Trong,Ahn, Kyoung-Kwan 제어로봇시스템학회 2010 Transaction on control, automation and systems eng Vol. No.
A radial basis function neural network sliding-mode controller (RBFSMC) is proposed to control a shape memory alloy (SMA) actuator. This approach, which combines a RBF neural network with sliding-mode control (SMC), is presented for the tracking control of a class of nonlinear systems having parameter uncertainties. The centers and output weights of the RBF neural network are updated through on-line learning, which causes the output of the neural network control to approximate the sliding-mode equivalent control along the direction that makes the sliding-mode asymptotically stable. Using Lyapunov theory, the asymptotic stability of the overall system is proven. Then, the controller is applied to compensate for the hysteresis phenomenon seen in SMA. The results show that the controller was applied successfully. The control results are also compared to those of a conventional SMC.
-
A RBF Neural Network Sliding Mode Controller for SMA Actuator
Nguyen Trong Tai,안경관 제어·로봇·시스템학회 2010 International Journal of Control, Automation, and Vol.8 No.6
A radial basis function neural network sliding-mode controller (RBFSMC) is proposed to control a shape memory alloy (SMA) actuator. This approach, which combines a RBF neural network with slid-ing-mode control (SMC), is presented for the tracking control of a class of nonlinear systems having parameter uncertainties. The centers and output weights of the RBF neural network are updated through on-line learning, which causes the output of the neural network control to approximate the sliding-mode equivalent control along the direction that makes the sliding-mode asymptotically stable. Using Lyapunov theory, the asymptotic stability of the overall system is proven. Then, the controller is applied to compensate for the hysteresis phenomenon seen in SMA. The results show that the control-ler was applied successfully. The control results are also compared to those of a conventional SMC.
-
Model Predictive Control for Shape Memory Alloy Cylinder
Nguyen Trong Tai,Nguyen Bao Kha,Kyoung Kwan Ahn 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In this paper, a linear lightweight electric cylinder using SMA (Shape Memory Alloy) is proposed. The spring SMA is used as the actuator to control the position of the cylinder. The cylinder position is controlled by Model Predictive Control algorithm. In this controller, the cylinder model is estimated by online identification algorithm, so that SMA hysteresis effect will be compensated. Experimental results show that the position of the SMA cylinder is able to control precisely by using predictive control strategy though the hysteresis effect existing in this actuator. The performance of the proposed controller is also compared with the conventional PID controller and shown in this paper.??
-
Predictive position and force control for shape memory alloy cylinders
Nguyen Trong Tai,Nguyen Bao Kha,안경관 대한기계학회 2010 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.24 No.8
In this paper, a linear lightweight electric cylinder constructed using shape memory alloy (SMA) is proposed. Spring SMA is used as the actuator to control the position and force of the cylinder rod. The model predictive control algorithm is investigated to compensate SMA hysteresis phenomenon and control the cylinder. In the predictive algorithm, the future output of the cylinder is computed based on the cylinder model, and the control signal is computed to minimize the error and power criterion. The cylinder model parameters are estimated by an online identification algorithm. Experimental results show that the SMA cylinder is able to precisely control position and force by using the predictive control strategy though the hysteresis effect existing in the actuator. The performance of the proposed controller is compared with that of a conventional PID controller.
-
A TabNet - Based System for Water Quality Prediction in Aquaculture
Trong-Nghia Nguyen,김수형(Soo Hyung Kim),도누따이(Nhu-Tai Do),Thai-Thi Ngoc Hong,양형정(Hyung Jeong Yang),이귀상(Guee Sang Lee) 한국스마트미디어학회 2022 스마트미디어저널 Vol.11 No.2
In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
