In the design of IMC controller or other robust controller for robust performance in process control application, one of the necessary conditions is model (or process) uncertainty bound. The method of calculating mode...In the design of IMC controller or other robust controller for robust performance in process control application, one of the necessary conditions is model (or process) uncertainty bound. The method of calculating model (or process) uncertainty bound for the first order lag process with dead time (FOPDT) in the robust controller design is reported in literatures. Up to the now, however, no any analytical method of calculating model (or process) uncertainty bound can be used in the second order lag process with dead time (SOPDT). Therefore, the design of the IMC controller for robust performance used to SOPDT also can not be simply achieved.By using first order Pade approximation for the dead time,an analytical method of calculating model (or process) uncertainty bound for SOPDT in the robust controller design is given for the first time.By using this bound, IMC controller design for robust performance can be made satisfactorily.展开更多
A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rat...A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.展开更多
文摘In the design of IMC controller or other robust controller for robust performance in process control application, one of the necessary conditions is model (or process) uncertainty bound. The method of calculating model (or process) uncertainty bound for the first order lag process with dead time (FOPDT) in the robust controller design is reported in literatures. Up to the now, however, no any analytical method of calculating model (or process) uncertainty bound can be used in the second order lag process with dead time (SOPDT). Therefore, the design of the IMC controller for robust performance used to SOPDT also can not be simply achieved.By using first order Pade approximation for the dead time,an analytical method of calculating model (or process) uncertainty bound for SOPDT in the robust controller design is given for the first time.By using this bound, IMC controller design for robust performance can be made satisfactorily.
基金Project(2013CB733605)supported by the National Basic Research Program of ChinaProject(21176073)supported by the National Natural Science Foundation of China
文摘A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.
