Control method of PID controller

Abstract

The invention relates to a control method of a PID controller. The method comprises the following steps of S1) selecting a structure of a BP neural network, determining a number of nodes of each layer in the neural network, initializing initial values of weight coefficients, then selecting a learning rate eta and an inertia coefficient alpha, and making k equals 1, wherein the BP neural network includes an input layer, a hidden layer and an output layer; S2) sampling to acquire r(k) and y(k), and calculating the following equation: e(k)=z(k)=r(k)-y(k), wherein the r(k) is reference input and the y(k) is system output; S3) carrying out normalization processing on r(i), y(i) and e(i) and taking as input of the BP network, wherein i=k, k-1,..., k-p; S4) carrying out forward direction calculation on each layer input and output of the BP neural network, wherein output of the output layer is three adjustable parameters kp, ki and kd of the PID controller; S5) calculating control output u(k) of the PID controller and making the u(k) participate in the following further control and calculating; S6) adjusting the weight coefficients of each layer; and S7) setting an equation: k=k+1 and returning to the S2 to operate continuously.

Description

technical field [0001] The invention relates to a control method of a PID controller. Background technique [0002] The traditional PID controller has the advantages of simple structure, robustness to model errors and easy operation, and is widely used in industrial process control fields such as metallurgy, chemical industry, electric power, light industry and machinery. With the development of industry, the complexity of the controlled object is increasing, especially for the large lag, time-varying, nonlinear complex system, the traditional PID control has been unable to meet the requirements of precise target control. In traditional PID control, the conventional method is to use manual setting or offline tuning. When the structure or parameter characteristics of the control object change (such as changes in external conditions, or intervention by external disturbances, etc.), the PID parameters cannot be corrected in time, making It is difficult for the PID controller t...

AI Technical Summary

Problems solved by technology

However, PID control parameters are often obtained through empirical formulas or trial and error methods, and its control parameters can only be ideal for the control effect under a certain condition, and there is no such "adaptive" or "automatic" control parameters. The ability to "adjust" can only rely on man-made re-adjustment of a set of more appropriate control parameters

However, due to the real-time, complexity and continuity of the control process, it is very difficult to manually readjust the control parameters in real time, so the conventional PID controller has problems such as difficult parameter adjustment and poor anti-interference ability.

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