Data-based sewage treatment system self-learning trajectory tracking method

Abstract

The invention provides a data-based sewage treatment system self-learning trajectory tracking method, which realizes a self-learning optimal tracking control algorithm of a non-affine nonlinear systemby using a DHP structure, and is applied to sewage treatment process control in rainstorm weather. According to the method, a numerical method is adopted to solve stability control corresponding to an expected trajectory, and then a data-driven self-learning method for solving the optimal control law of the non-affine system based on iterative DHP is established. The method is applied to concentration control of dissolved oxygen and nitrate nitrogen so as to achieve a good trajectory tracking effect of a sewage treatment system.

Description

technical field [0001] The invention belongs to the technical field of sewage treatment, in particular to a data-based self-learning trajectory tracking method for a sewage treatment system. Background technique [0002] Water is the basic resource on which all life depends, including human beings, and plays an important role in the process of human economic and social development. my country is a country that lacks fresh water resources, and the per capita fresh water resources are only a quarter of the world's. With the continuous expansion of the scale of urbanization in our country, the amount of sewage discharge has increased year by year, resulting in more and more serious water pollution problems. How to realize the sustainable use of water resources is becoming more and more important. Among them, urban sewage treatment is an important means to improve water shortage and water pollution. However, many sewage treatment plants in our country have insufficient technic...

AI Technical Summary

Problems solved by technology

Although adaptive dynamic programming has good self-learning and self-adaptive capabilities, and there have been many studies on discrete-time affine systems, there is a lack of systematic research results on the optimal tracking control of non-affine unknown systems. Especially in the field of sewage treatment process control, the application is still relatively small

In particular, the existing research is generally aimed at the control design of sewage treatment in sunny and rainy days, and there are few intelligent tracking control technologies for sewage treatment process in rainy weather, and there is no reasonable design of stable control corresponding to the expected trajectory.

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