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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.
Load Voltage Tracking of Electric Power Systems by Using Backstepping Design
Der-Cherng Liaw,Shih-Tse Chang,Yun-Hua Huang,Jen-Tze Huang,Tzu-Chung Yenn 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
A backstepping controller design is proposed for the load voltage tracking of electric power system. It is known that a power system might exhibit saddle-node and Hopf bifurcations as load change, which can lead to the appearance of dynamic and/or static voltage collapses. The output tracking control laws are proposed for the load voltage regulation of the electric power system via backstepping control scheme. The numerical simulations demonstrate that the proposed control scheme not only could provide the regulation of the load voltage but also prevent and/or delay the appearance of bifurcation phenomena and chaotic behavior.
of Midcourse Guidance Laws via a Combination of Fuzzy and SMC Approaches
Yew-Wen Liang,Chun-Hone Chen,Der-Cherng Liaw,Shih-Tse Chang,Sheng-Dong Xu 제어·로봇·시스템학회 2010 International Journal of Control, Automation, and Vol.8 No.2
Issues regarding the design of midcourse guidance laws for antimissiles are addressed. The antimissile is expected to be guided to a place with a desired direction, where a ballistic missile is predicted to pass in the reverse direction, so that the target can be easily found and locked for terminal interception. The predicted location and direction of a ballistic missile may vary with time, due to information update using a trajectory prediction algorithm. To fulfill the guidance performance, the guidance laws are designed by combining the Takagi-Sugeno (T-S) fuzzy approach and the Sliding Mode Control (SMC) technique. Under the designed guidance law, it is shown that the antimissile is able to be efficiently guided to a specified location and direction, even when the existence of uncertainties and disturbances.