Because of being imprecision and existence of uncertainty in input variables to fuzzy systems, and also their easy implementation, fuzzy controllers are introduced as one of useful optimization tools in industry especially in DC motors. Given to the growth of controller systems usages in industry, use of optimization methods has been noticed in the recent years; so that improve precision and performance of these systems. In addition to making improvement in their performance, real time implementation, less energy spending in comparison with other tools, having high speed in mathematical computation and decreasing hardware resources consumption are some serious challenges in this terrain. To optimize fuzzy controllers’ performance, various methods have been proposed by the researchers. This paper firstly focuses on applying improved Genetic Algorithm in regulating optimum parameters of fuzzy controller to rise convergence speed and accuracy. Secondly, a pipeline technique with specific strategies of diminishing required bit width for fuzzy controllers is provided to achieve maximum efficiency in fuzzy controller implementation. In general, it can be seen that optimized fuzzy controller in this paper has precise performance, high convergence speed and such advantages in efficient hardware implementation in comparison with other fuzzy controllers.

由于模糊系统的输入变量不精确且存在不确定性，并且易于实现，因此模糊控制器作为工业上特别是直流电动机中有用的优化工具之一被引入。随着工业中控制器系统使用量的增长，近年来已经注意到优化方法的使用。从而提高这些系统的精度和性能。除了改善其性能外，实时实施，与其他工具相比耗能更少，数学计算速度快以及硬件资源消耗减少是该领域的严峻挑战。为了优化模糊控制器的性能，研究人员提出了各种方法。本文首先将改进遗传算法应用于模糊控制器的最优参数调节，以提高收敛速度和精度。其次，提供了一种具有减少模糊控制器所需位宽的特定策略的流水线技术，以实现模糊控制器实现的最大效率。总体上可以看出，与其他模糊控制器相比，本文优化的模糊控制器具有精确的性能，较高的收敛速度以及在高效硬件实现上的优势。