Abstract
In this article, a genetic algorithm–based proportional integral differential–type fuzzy logic controller for speed control of brushless direct current motors is presented to improve the performance of a conventional proportional integral differential controller and a fuzzy proportional integral differential controller, which consists of a genetic algorithm–based fuzzy gain tuner and a conventional proportional integral differential controller. The tuner is used to adjust the gain parameters of the conventional proportional integral differential controller by a new fuzzy logic controller. Different from the conventional fuzzy logic controller based on expert experience, the proposed fuzzy logic controller adaptively tunes the membership functions and control rules by using an improved genetic algorithm. Moreover, the genetic algorithm utilizes a novel reproduction operator combined with the fitness value and the Euclidean distance of individuals to optimize the shape of the membership functions and the contents of the rule base. The performance of the genetic algorithm–based proportional integral differential–type fuzzy logic controller is evaluated through extensive simulations under different operating conditions such as varying set speed, constant load, and varying load conditions in terms of overshoot, undershoot, settling time, recovery time, and steady-state error. The results show that the genetic algorithm–based proportional integral differential–type fuzzy logic controller has superior performance than the conventional proportional integral differential controller, gain tuned proportional integral differential controller, conventional fuzzy proportional integral differential controller, and scaling factor tuned fuzzy proportional integral differential controller.
Highlights
Owing to the high reliability, high efficiency, noise-free operation, long operating lifetime, and low maintenance, brushless direct current (BLDC) motors have been widely used in robots,[1] underwater glider,[2] electric vehicles,[3,4,5] aerospace,[6] and other fields
Proportional integral (PI), proportional differential (PD), or proportional integral differential (PID) controller is the preferable method for speed control of BLDC motors[7,10,11,12,13] because of its simple structure, strong robustness, and good applicability
The objective of this article is to design a new fuzzy PID controller for the speed control of the BLDC motor, and an improved genetic algorithm (GA) is used to choose the optimal membership functions and control rules, so as to make the fuzzy PID controller simpler and more efficient compared to conventional proportional integral differential (C-PID) controller,[13] conventional fuzzy proportional integral differential controller (C-PID-FLC),[7] gain tuned proportional integral differential (G-PID) controller[19] and scaling factor tuned fuzzy PID based on GA (tuned fuzzy proportional integral differential controller (TPID-FLC)).[24]
Summary
Owing to the high reliability, high efficiency, noise-free operation, long operating lifetime, and low maintenance, brushless direct current (BLDC) motors have been widely used in robots,[1] underwater glider,[2] electric vehicles,[3,4,5] aerospace,[6] and other fields. The existing uncertainties, nonlinearity, and manually tuned parameters of a typical PI, PD, or PID controller make it difficult to determine the appropriate gains to achieve the optimal performance of the control system.[9,13] intelligent algorithms such as particle swarm,[7] fuzzy logic control,[10] differential evolution,[11] neural network,[14,15,16] neuro fuzzy,[9] and sliding mode control[17,18] are proposed to adjust the gains of the PID controller so as to improve the performance of the BLDC motors. The adjustment method based on fuzzy logic control has better control effect than the other approaches such as neural network and sliding mode control in most cases.[10]
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