Intelligence Proportional-integral-aifferential control method with quantitative positive definite robustness

Abstract

An intelligent proportional-integral-derivative control method for quantitatively setting up the robustness of system includes such steps as recognizing the model of controlled object by the industrial control system, generally judging the corresponding model error value by user according to the in-situ condition, executing the programmed intelligent PID control program by monitor module, evaluating the optimal controller parameters, and calculating PID controller parameters to obtain the control signal. It can quantitatively set up the system robustness and make a compromise between symmetricity and robustness of the system.

Description

Technical field: [0001] The invention relates to an intelligent proportional-integral-derivative control method for quantitatively adjusting the robustness. For industrial process control objects, based on the optimal control theory and the robust control theory, the robustness of the system can be quantitatively adjusted, and the use is very simple and convenient. It belongs to the technical field of industrial process control. Background technique: [0002] In industrial process control, more than 95% of the control loops are in the form of PID. The reason why the PID controller is the most widely used and mature is that on the one hand, it has a simple and fixed form, allowing engineers and technicians to adjust the system in a direct way; on the other hand, because it operates in a wide range It can maintain good robustness in the range of conditions. [0003] At present, the most used PID controller tuning method is the traditional method based on experience. Typical...

AI Technical Summary

Problems solved by technology

Generally speaking, the above-mentioned PID tuning methods rarely take into account the uncertainty of the control object. When tuning the controller, engineers and technicians need to adjust three parameters at the same time, and there is a lack of correlation between these three parameters and system performance indicators. Clear connection, of course, the lack of connection between these three parameters and the model error of the controlled object, so people can neither know how to effectively adjust the parameters to achieve the specified performance index, nor know whether the system has better robustness after tuning Moreover, when the model error changes due to the control object being disturbed or other reasons, or when the control requirements of the system change, it is not known how to effectively adjust the controller parameters to achieve a compromise between system performance and robustness

[0006] Conventional methods fail to design controllers with specified performance and robustness

Among the many optimal analytical design methods developed later, the main problem in designing controllers with state-space robust control methods is that the range of uncertainty needs to be known, and it is difficult to design controllers according to specified performance and robustness. When the performance range changes, the controller needs to be redesigned, and the controller is more complicated

From another perspective, Lee et al. proposed a design method of PID controller (hereinafter referred to as MacPID controller) based on Maelaurin expansion (Lee, Y., Park, S., Lee, M., and Brosilow, C. , PID controller tuning for desired closedloop response for SISO systems.AIChE J. 1998, 44, 1, pp.106-115), this method can achieve better system performance in theory, but no tuning method is given

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