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Huan-Chan Ting,Jeang-Lin Chang,Yon-Ping Chen 제어·로봇·시스템학회 2011 International Journal of Control, Automation, and Vol.9 No.6
For time-delay systems with mismatched disturbances and uncertainties, this paper develops an integral sliding mode control algorithm using output information only to stabilize the systems. An integral sliding surface is comprised of output signals and an auxiliary full-order compensator. The proposed output feedback sliding mode controller can satisfy the reaching and sliding condition and maintain the system on the sliding surface from the initial moment. When two specific algebraic Ric-cati inequalities have solutions, our method can guarantee the stability of the closed-loop system and satisfy the property of robust disturbance attenuation. Moreover, the design parameters of controller and compensator can be simultaneously determined by solutions to the algebraic Riccati inequalities. Finally, two numerical examples illustrate the applicability of the proposed scheme.
Huan-Chan Ting,Jeang-Lin Chang,Yon-Ping Chen 제어·로봇·시스템학회 2011 International Journal of Control, Automation, and Vol.9 No.5
This paper considers the problem of estimating the state of an MIMO linear system with unknown inputs in the state and output. Through a series of linear transformations in the state and output equations, the original system can be transformed into a descriptor system form. The proposed propor-tional derivative observer can accurately estimate the system state and avoid the peaking phenomenon. Moreover, the approach developed in this paper does not require the derivatives of the output and can be applied to the system with unstable zeros (with respect to the relation between the output and the unknown input). Finally, our algorithm can prove the valid feasibility and the property of disturbance attenuation through demonstrating a simulation-base example.
Stabilization of selenium cathodes via in situ formation of protective solid electrolyte layer
Lee, Jung Tae,Kim, Hyea,Nitta, Naoki,Eom, Kwang-sup,Lee, Dong-Chan,Wu, Feixiang,Lin, Huan-Ting,Zdyrko, Bogdan,Cho, Won Il,Yushin, Gleb The Royal Society of Chemistry 2014 Journal of Materials Chemistry A Vol.2 No.44
Grey Neural Network-Based Forecasting System for Vision-Guided Robot Trajectory Tracking
Shih-Hung Yang,Chung-Hsien Chou,Chen-Fang Chung,Wen-Pang Pai,Tse-Han Liu,Yung-Sheng Chang,Jung-Che Li,Huan-Chan Ting,Yon-Ping Chen 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10
This paper presents a grey neural network-based forecasting system (GNNFS) in solving the prediction problem. GNNFS adopts a grey model to predict the signal and a neural network (NN) to forecast the prediction error of the grey model. A sequential batch learning (SBL) is developed to adjust the weights of the NN. The proposed GNNFS is applied to a binocular robot, called an Eye-Robot, for human-robot interaction which involved predicting the trajectory of a participant’s hand and tracking the hand. By applying the SBL, the GNNFS can gradually learn to predict the trajectory of the hand and track it well. The experimental results show that the GNNFS can carry out the SBL in real-time for vision-guided robot trajectory tracking.