A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimiz...A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs.展开更多
The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various faul...The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. The used flexible alternating current transmission system （FACTS） in this paper is an advanced super-conducting magnetic energy storage （ASMES）. Many control techniques that use ASMES to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES have been actively reported. However, it is sometimes very difficult to specify the rule base for some plants, when the parameters change. To solve this problem, a fuzzy model reference learning controller （FMRLC） is proposed in this paper, which investigates multi-input multi-output FMRLC for time-variant nonlinear system. This control method provides the motivation for adaptive fuzzy control, where the focus is on the automatic online synthesis and tuning of fuzzy controller parameters （i.e., using online data to continually learn the fuzzy controller that will ensure that the performance objectives are met）. Simulation results show that the proposed robust controller is able to work with nonlinear and nonstationary power system （i.e., single machine-infinite bus （SMIB） system）, under various fault conditions and disturbances.展开更多
Model reference adaptive system(MRAS)is typically employed for rotor position/speed estimation in sensorless interior permanent magnet motor(IPMSM)drives.The adjustment of control parameters in MRAS is a key issue for...Model reference adaptive system(MRAS)is typically employed for rotor position/speed estimation in sensorless interior permanent magnet motor(IPMSM)drives.The adjustment of control parameters in MRAS is a key issue for IPMSM drive systems with cyclic fluctuating loads.In order to avoid the difficulties involved with manual tuning of the control parameters,a new MRAS scheme based on fuzzy logic is proposed in this paper in which a fuzzy controller replaces the conventional PI regulator.To implement this new MRAS scheme,a two-dimensional(2-D)fuzzy rule is designed.The proposed control scheme is employed in the IPMSM drives with cyclic fluctuating loads such as compressors.In order to lower the motor speed ripple caused by the cyclic fluctuating load,a feed-forward compensation strategy with the load-matching motor output torque pattern is developed.Experimental results demonstrate the feasibility and effectiveness of the proposed fuzzy logic based MRAS scheme with minimal rotor position estimation error.展开更多
Under the trends to using renewable energy sources as alternatives to the traditional ones,it is important to contribute to the fast growing development of these sources by using powerful soft computing methods.In thi...Under the trends to using renewable energy sources as alternatives to the traditional ones,it is important to contribute to the fast growing development of these sources by using powerful soft computing methods.In this context,this paper introduces a novel structure to optimize and control the energy produced from a variable speed wind turbine which is based on a squirrel cage induction generator(SCIG)and connected to the grid.The optimization strategy of the harvested power from the wind is realized by a maximum power point tracking(MPPT)algorithm based on fuzzy logic,and the control strategy of the generator is implemented by means of an internal model(IM)controller.Three IM controllers are incorporated in the vector control technique,as an alternative to the proportional integral(PI)controller,to implement the proposed optimization strategy.The MPPT in conjunction with the IM controller is proposed as an alternative to the traditional tip speed ratio(TSR)technique,to avoid any disturbance such as wind speed measurement and wind turbine(WT)characteristic uncertainties.Based on the simulation results of a six KW-WECS model in Matlab/Simulink,the presented control system topology is reliable and keeps the system operation around the desired response.展开更多
基金the National Natural Science Foundation of China (No.50579007)
文摘A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs.
文摘The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. The used flexible alternating current transmission system （FACTS） in this paper is an advanced super-conducting magnetic energy storage （ASMES）. Many control techniques that use ASMES to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES have been actively reported. However, it is sometimes very difficult to specify the rule base for some plants, when the parameters change. To solve this problem, a fuzzy model reference learning controller （FMRLC） is proposed in this paper, which investigates multi-input multi-output FMRLC for time-variant nonlinear system. This control method provides the motivation for adaptive fuzzy control, where the focus is on the automatic online synthesis and tuning of fuzzy controller parameters （i.e., using online data to continually learn the fuzzy controller that will ensure that the performance objectives are met）. Simulation results show that the proposed robust controller is able to work with nonlinear and nonstationary power system （i.e., single machine-infinite bus （SMIB） system）, under various fault conditions and disturbances.
基金Supported by National Natural Science Foundation of China under Grant 51477003Beijing Natural Science Foundation under Grant 4152013.
文摘Model reference adaptive system(MRAS)is typically employed for rotor position/speed estimation in sensorless interior permanent magnet motor(IPMSM)drives.The adjustment of control parameters in MRAS is a key issue for IPMSM drive systems with cyclic fluctuating loads.In order to avoid the difficulties involved with manual tuning of the control parameters,a new MRAS scheme based on fuzzy logic is proposed in this paper in which a fuzzy controller replaces the conventional PI regulator.To implement this new MRAS scheme,a two-dimensional(2-D)fuzzy rule is designed.The proposed control scheme is employed in the IPMSM drives with cyclic fluctuating loads such as compressors.In order to lower the motor speed ripple caused by the cyclic fluctuating load,a feed-forward compensation strategy with the load-matching motor output torque pattern is developed.Experimental results demonstrate the feasibility and effectiveness of the proposed fuzzy logic based MRAS scheme with minimal rotor position estimation error.
文摘Under the trends to using renewable energy sources as alternatives to the traditional ones,it is important to contribute to the fast growing development of these sources by using powerful soft computing methods.In this context,this paper introduces a novel structure to optimize and control the energy produced from a variable speed wind turbine which is based on a squirrel cage induction generator(SCIG)and connected to the grid.The optimization strategy of the harvested power from the wind is realized by a maximum power point tracking(MPPT)algorithm based on fuzzy logic,and the control strategy of the generator is implemented by means of an internal model(IM)controller.Three IM controllers are incorporated in the vector control technique,as an alternative to the proportional integral(PI)controller,to implement the proposed optimization strategy.The MPPT in conjunction with the IM controller is proposed as an alternative to the traditional tip speed ratio(TSR)technique,to avoid any disturbance such as wind speed measurement and wind turbine(WT)characteristic uncertainties.Based on the simulation results of a six KW-WECS model in Matlab/Simulink,the presented control system topology is reliable and keeps the system operation around the desired response.
