The attitude adjustment of rope system faces the challenging due to the difficulty in obtaining accurate three-dimensional(3D)mathematical model and solving by traditional methods.A set of adjustment systems is design...The attitude adjustment of rope system faces the challenging due to the difficulty in obtaining accurate three-dimensional(3D)mathematical model and solving by traditional methods.A set of adjustment systems is designed and used to investigate the automatic control for level or preset attitude adjustment of unknown weights and eccentric loads.The system principle and characteristics are analyzed.The 3D model is decomposed into two two-dimensional(2D)subsystems,and an adaptive fuzzy controller based on BP neural network and least squares(LSE)is designed.The simulation experiment uses MATLAB to train the level-adjustment data for testing algorithm,and a small load is used to verify the effectiveness of the system.The experimental results show that precise attitude adjustment can be achieved within the system load range,and the response speed is fast.This adjustment method provides a fast and effective method for precise adjustment of the load attitude.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
The control of heat exchange stations in district heating system is critical for the overall energy efficiency and can be very difficult due to high level of complexity. A conventional method is to control the equipme...The control of heat exchange stations in district heating system is critical for the overall energy efficiency and can be very difficult due to high level of complexity. A conventional method is to control the equipment such that the temperature of hot water supply is maintained at a set-point that may be a fixed value or be compensated against the external temperature. This paper presents a novel scheme that can determine the optimal set-point of hot water supply that maximizes the energy efficiency whilst providing sufficient heating capacity to the load. This scheme is based on Adaptive Neuro-Fuzzy Inferential System. The aim of this study is to improve the overall performance of district heating systems.展开更多
基金National Natural Science Foundation of China(No.61605177)
文摘The attitude adjustment of rope system faces the challenging due to the difficulty in obtaining accurate three-dimensional(3D)mathematical model and solving by traditional methods.A set of adjustment systems is designed and used to investigate the automatic control for level or preset attitude adjustment of unknown weights and eccentric loads.The system principle and characteristics are analyzed.The 3D model is decomposed into two two-dimensional(2D)subsystems,and an adaptive fuzzy controller based on BP neural network and least squares(LSE)is designed.The simulation experiment uses MATLAB to train the level-adjustment data for testing algorithm,and a small load is used to verify the effectiveness of the system.The experimental results show that precise attitude adjustment can be achieved within the system load range,and the response speed is fast.This adjustment method provides a fast and effective method for precise adjustment of the load attitude.
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.
文摘The control of heat exchange stations in district heating system is critical for the overall energy efficiency and can be very difficult due to high level of complexity. A conventional method is to control the equipment such that the temperature of hot water supply is maintained at a set-point that may be a fixed value or be compensated against the external temperature. This paper presents a novel scheme that can determine the optimal set-point of hot water supply that maximizes the energy efficiency whilst providing sufficient heating capacity to the load. This scheme is based on Adaptive Neuro-Fuzzy Inferential System. The aim of this study is to improve the overall performance of district heating systems.
