PID controller parameter control method of object in thermal process

Abstract

The invention discloses a PID controller parameter control method of an object in a thermal process. The method comprises the following steps of: S1: establishing a high-order transfer function model of an object; S2: establishing an equation by using an asymptote of an open-loop transfer function Nyquist curve, and determining the relationship between the proportional gain kp and the integral time constant Ti in the PID controller parameters and the high-order transfer function model parameters of the object; S3: determining the relation between the integral time constant Ti and the order n of the high-order transfer function model and the time constant T of the high-order transfer function model; and S4: determining the relation between the derivative time constant Td and the integral time constant Ti in the PID controller. The PID controller parameter control method is simple, convenient, rapid and has good setting effect.

Description

technical field [0001] The invention relates to a thermal process, in particular to a PID controller parameter control method for an object in the thermal process. Background technique [0002] PID control technology is a mature and widely used control strategy in control technology. So far, many complete control methods and typical structures have been accumulated. In addition, PID control technology can be applied not only to control systems with known mathematical models, but also to most industrial processes whose mathematical models are not determined. [0003] But the parameters of the traditional controller are adjusted through the experience accumulated in the thermal production process, so it is difficult to achieve good control effect. However, the existing tuning methods such as genetic algorithm have slow calculation speed and complicated programming language; the search radius of particle swarm algorithm decreases in the later stage, resulting in slower converg...

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Problems solved by technology

[0003] However, the parameters of the traditional controller are adjusted through the experience accumulated in the thermal production process, and it is difficult to obtain a good control effect

However, the existing tuning methods such as genetic algorithm have slow calculation speed and complicated programming language; the search radius of particle swarm algorithm decreases in the later stage, resulting in slower convergence speed and longer calculation time

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