It has been found that for a fixed degree of fuzziness in the coarsened references of measurements,the quantum-toclassical transition can be observed independent of the macroscopicity of the quantum state.We explore a...It has been found that for a fixed degree of fuzziness in the coarsened references of measurements,the quantum-toclassical transition can be observed independent of the macroscopicity of the quantum state.We explore a general situation that the degree of fuzziness can change with the rotation angle between two states（different rotation angles represent different references）.The fuzziness of reference comes from two kinds of fuzziness：the Hamiltonian（rotation frequency）and the timing（rotation time）.For the fuzziness of the Hamiltonian alone,the degree of fuzziness for the reference will change with the rotation angle between two states,and the quantum effects can still be observed with any degree of fuzziness of Hamiltonian.For the fuzziness of timing,the degree of the coarsening reference is unchanged with the rotation angle.During the rotation of the measurement axis,the decoherence environment can also help the classical-to-quantum transition due to changing the direction of the measurement axis.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
基金Project supported by the National Natural Science Foundation of China(Grant No.11375168)
文摘It has been found that for a fixed degree of fuzziness in the coarsened references of measurements,the quantum-toclassical transition can be observed independent of the macroscopicity of the quantum state.We explore a general situation that the degree of fuzziness can change with the rotation angle between two states（different rotation angles represent different references）.The fuzziness of reference comes from two kinds of fuzziness：the Hamiltonian（rotation frequency）and the timing（rotation time）.For the fuzziness of the Hamiltonian alone,the degree of fuzziness for the reference will change with the rotation angle between two states,and the quantum effects can still be observed with any degree of fuzziness of Hamiltonian.For the fuzziness of timing,the degree of the coarsening reference is unchanged with the rotation angle.During the rotation of the measurement axis,the decoherence environment can also help the classical-to-quantum transition due to changing the direction of the measurement axis.
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.
