摘要
Subject: The Brushless DC Motor speed control system design
based on the Fuzzy Control
Specialty: Control Theory and Control Engineering
Name: Zhou Wan-shun
Supervisor: Zhang Lei Associate Professor
ABSTRACT
The brushless DC motor has been widely used in fields of electromechanical
energy conversion because of its small size, high power density, simple structure and
better speed regulation performance, etc.
Brushless DC motor has multi-variable, strong coupling, nonlinear and other
characteristics, so it is difficult to find a suitable PID parameter online and accurate
control to achieve the ideal effect. Adaptive control algorithm can identify and
distinguish parameter, estimate motor status relative to linear model. It is unable to
obtain satisfactory control effect for nonlinear system. Fuzzy control doesn’t require
grasp the precise model of the controlled object, a lot of expert control rules are unable
to satisfy the control requirements of different objects. Clonal selection algorithm can
effectively search the global optimal solution, and avoid falling into local optimal
solution. In this paper, a variety of control strategies are combined with each other, a
fuzzy adaptive PID controller based on clonal selection algorithm is designed.
This paper describes the structure and operation principle of brushless DC motor,
then establishes double closed loop control system by learning its mathematical model.
The outer loop module utilize the fuzzy adaptive PID controller which has been
optimized by multi-objective clonal selection algorithm, the inner module utilize the
traditional PI controller. In addition, this paper presents two kinds of control methods,
one is optimizes the fuzzy rules by clonal selection algorithm, the other is control by
elitist-guidance mechanism. Both control method can improve the performance of the
brushless DC motor. Compared with the conventional controller, the system response
time has improved obviously, and be able to reach steady state quickly, it is also has
higher control accuracy relative to the conventional controller. Though the first method
can obtain the fuzzy rules on the overall optimal, it unable to be satisfied with
decision-making preferences. The second method can make a fast, effective,
directional search for Pareto optimal solutions according to decision-making
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