Traditional PID controller has the characteristics of simple arithmetic and high control precision, but it can not achieve satisfactory control effect for the system with random disturbance and delay. Fuzzy controller has the advantages of adapting to the non-linearity and time-varying of the controlled object, but the control accuracy is not high. Therefore, a fuzzy-PID controller is designed by combining fuzzy control with PID control, and the simulation is carried out by using SimuLink toolbox in matlab.
The results show that the fuzzy-PID controller has better dynamic performance and robustness than the traditional PID controller. PID controller is one of the earliest developed control strategies. Because of its simple control algorithm, good robustness and high reliability, it is widely used in industrial process control.
But for the control object which is difficult to establish an accurate mathematical model, the traditional PID can not achieve the desired control effect. Fuzzy control is a computer control method based on fuzzy set theory, fuzzy linguistic variables and fuzzy logic reasoning. As an important branch of intelligent control, it has been widely used in the field of control.
Fuzzy controller can be used to adjust the parameters of the PID controller and give full play to the advantages of the fuzzy controller and the PID controller. Points, so that the system to achieve the best control effect.
In this paper, the fuzzy PID controller is designed and simulated. Fuzzy PID controller is composed of two parts: fuzzy reasoning and PID controller. Its structure is shown in Figure 1. The principle is that the deviation E and the deviation change rate de/dt of the input PID regulator are simultaneously input into the fuzzy controller, and the three parameters KP, KI and KD are adjusted. After fuzzification, approximate reasoning and clarification, the corrections KP, KI and KD are input into the PID regulator respectively, and the three coefficients are corrected on-line in real time. The two-dimensional Mamdani controller is used in the fuzzy controller, Max-Min is used in the decision-making of the fuzzy control, and the center of gravity method is used in the solution of the fuzzy control. The fuzzy controller takes | e | and | EC | as input language variables and KP, KI and KD as output language variables.
The fuzzy subsets of input and output language variables are {NB, thermostatic element NM, NS, ZO, PS, PM, PB} [5]. Their membership function curves are shown in Figure 2. The fields of E, EC are [-3,3], KP, KI and KD are [-0.3, 0.3], [-0.06, 0.06], [-3, 3], except that the membership function of NB is zmf function, the others are trimf functions. The membership function curve is shown in Figure 2.
When | e | is large, in order to make the system have better tracking performance, we should take larger KP and smaller KD. At the same time, in order to avoid large overshoot, we should limit the integral effect, usually take KI = 0. When | e | is of medium size, KP should be smaller in order to make the system have a smaller overshoot. In this case, the value of KD has a greater impact on the system, should be smaller, the value of KI should be appropriate. When | e | is small, both KP and KI should be larger in order to make the system have better stability.
At the same time, in order to avoid the oscillation of the system at the set value and consider the anti-jamming performance of the system, when | EC | is larger, KD may be smaller, and when | EC | is smaller, KD may be larger. SimuLink and Fuzzy toolbox in MATLAB are used to build the simulation system of traditional and fuzzy PID [8-10]. As shown in Figure 4, the transfer function of the controlled object system is set as G(s)=1/s2.
The step response of traditional PID and fuzzy PID simulation is shown in Fig.
5. The simulation results show that compared with the traditional PID, the fuzzy PID has faster response speed, almost no overshoot, shorter adjustment time and higher control accuracy.
In this paper, the fuzzy PID control and the traditional PID control are modeled and simulated by SimuLink in the toolbox of matlab.
The simulation results show that the fuzzy controller is used to adjust the parameters of the PID in real time. Compared with the traditional PID, better dynamic and steady-state performance and better robustness are obtained.