This article presents an optimal hybrid fuzzy proportion integral derivative （HFPID） controller based on combination of proportion integral derivative （PID） and fuzzy controllers, by which the parameters could be ...This article presents an optimal hybrid fuzzy proportion integral derivative （HFPID） controller based on combination of proportion integral derivative （PID） and fuzzy controllers, by which the parameters could be evaluated by global optimization either in convergence velocity or in convergence reliability. Focusing on the nonlinear factors of hydraulic servo system, this article takes advantage of PID and fuzzy logic controller integrated with scaling factors to acquire precise tracking performances. To further improve the performances, it provides new developed optimization with rapid convergence to attain reliable approach probability. Focusing on the performance indictors of evolutionary algorithm, this article presents a new technique to predict reliability of the optimization algorithm. Statistics authenticates the effectiveness and robustness of the optimization. Further, many simulation and experimental results indicate that the optimal HFPID could acquire perfect immunity against parametric uncertainties with external disturbance.展开更多
基金Hi-tech Research and Development Program of China (2009AA04Z412)Chinese Education Ministry Program 985 Ⅱ+1 种基金Program 111(B07009)Program for New Century Excellent Talents in University and Beijing Teaching Innovation Program (NCET-04-0618)
文摘This article presents an optimal hybrid fuzzy proportion integral derivative （HFPID） controller based on combination of proportion integral derivative （PID） and fuzzy controllers, by which the parameters could be evaluated by global optimization either in convergence velocity or in convergence reliability. Focusing on the nonlinear factors of hydraulic servo system, this article takes advantage of PID and fuzzy logic controller integrated with scaling factors to acquire precise tracking performances. To further improve the performances, it provides new developed optimization with rapid convergence to attain reliable approach probability. Focusing on the performance indictors of evolutionary algorithm, this article presents a new technique to predict reliability of the optimization algorithm. Statistics authenticates the effectiveness and robustness of the optimization. Further, many simulation and experimental results indicate that the optimal HFPID could acquire perfect immunity against parametric uncertainties with external disturbance.
