The Unified Power Flow Controller （UPFC） is one of the most versatile Flexible AC Transmission Systems （FACTS） devices that has unique capability of independently controlling the real and reactive power flows, in ...The Unified Power Flow Controller （UPFC） is one of the most versatile Flexible AC Transmission Systems （FACTS） devices that has unique capability of independently controlling the real and reactive power flows, in addition to regulate the system bus voltage. This paper presents performance analysis of Unified Power Flow Controller based on two axis theory. Based on this analysis, a new Artificial Neural Network （ANN） based controller has been proposed to improve the system performance. The controller rules are structured depending upon the relationship between series inserted voltage and the desired changes in real/reactive power flow in the power system. The effects of different controllers along with parameters of series transformer and transmission line have been investigated through developed control block model in SIMULINK tool box of MATLAB. The effectiveness of the proposed scheme is demonstrated by case studies.展开更多
A learning controller of nonhonolomic robot in real-time based on support vector machine(SVM)is presented.The controller includes two parts:one is kinematic controller based on nonlinear law,and the other is dynamic c...A learning controller of nonhonolomic robot in real-time based on support vector machine(SVM)is presented.The controller includes two parts:one is kinematic controller based on nonlinear law,and the other is dynamic controller based on SVM.The kinematic controller is aimed to provide desired velocity which can make the steering system stable.The dynamic controller is aimed to transform the desired velocity to control torque.The parameters of the dynamic system of the robot are estimated through SVM learning algorithm according to the training data of sliding windows in real time.The proposed controller can adapt to the changes in the robot model and uncertainties in the environment.Compared with artificial neural network(ANN)controller,SVM controller can converge to the reference trajectory more quickly and the tracking error is smaller.The simulation results verify the effectiveness of the method proposed.展开更多
The proposed controller incorporates FL （fuzzy logic） algorithm with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorit...The proposed controller incorporates FL （fuzzy logic） algorithm with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorithm. A tuning method is proposed for training of the neuro-fuzzy controller. The best rule base and the best training algorithm chosen produced high performance in the ANFIS controller. Simulation was done on Matlab Ver. 2010a. A case study was chopper-fed DC motor drive, in continuous and discrete modes. Satisfactory results show the ANFIS controller is able to control dynamic highly-nonlinear systems. Tuning it further improved the results.展开更多
文摘The Unified Power Flow Controller （UPFC） is one of the most versatile Flexible AC Transmission Systems （FACTS） devices that has unique capability of independently controlling the real and reactive power flows, in addition to regulate the system bus voltage. This paper presents performance analysis of Unified Power Flow Controller based on two axis theory. Based on this analysis, a new Artificial Neural Network （ANN） based controller has been proposed to improve the system performance. The controller rules are structured depending upon the relationship between series inserted voltage and the desired changes in real/reactive power flow in the power system. The effects of different controllers along with parameters of series transformer and transmission line have been investigated through developed control block model in SIMULINK tool box of MATLAB. The effectiveness of the proposed scheme is demonstrated by case studies.
基金Project(60910005)supported by the National Natural Science Foundation of China
文摘A learning controller of nonhonolomic robot in real-time based on support vector machine(SVM)is presented.The controller includes two parts:one is kinematic controller based on nonlinear law,and the other is dynamic controller based on SVM.The kinematic controller is aimed to provide desired velocity which can make the steering system stable.The dynamic controller is aimed to transform the desired velocity to control torque.The parameters of the dynamic system of the robot are estimated through SVM learning algorithm according to the training data of sliding windows in real time.The proposed controller can adapt to the changes in the robot model and uncertainties in the environment.Compared with artificial neural network(ANN)controller,SVM controller can converge to the reference trajectory more quickly and the tracking error is smaller.The simulation results verify the effectiveness of the method proposed.
文摘The proposed controller incorporates FL （fuzzy logic） algorithm with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorithm. A tuning method is proposed for training of the neuro-fuzzy controller. The best rule base and the best training algorithm chosen produced high performance in the ANFIS controller. Simulation was done on Matlab Ver. 2010a. A case study was chopper-fed DC motor drive, in continuous and discrete modes. Satisfactory results show the ANFIS controller is able to control dynamic highly-nonlinear systems. Tuning it further improved the results.
