Direct Adaptive Neural Network Control for Dissolved Oxygen Concentration
| Autor: | Jiao-long Zhang, Rui-fei Bai, Wei Zhang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Lyapunov function
0209 industrial biotechnology Artificial neural network Computer science Control (management) 02 engineering and technology symbols.namesake 020901 industrial engineering & automation Control theory 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Pruning (decision trees) Oxygen saturation |
| Zdroj: | 2018 13th World Congress on Intelligent Control and Automation (WCICA). |
| DOI: | 10.1109/wcica.2018.8630484 |
| Popis: | In this paper, a direct adaptive neural-network control (DANC) method is proposed to control the dissolved oxygen (DO) concentration in the wastewater treatment process (WWTP), which employs the forward neural network to approximate an ideal control law. The main features of the DANC system include the following three aspects. Firstly, it is not necessary to establish the accurate plant model, which is favorable because it is an arduous task for the WWTP. Secondly, the multi-condition characteristic of WWTP is considered, and the neural network controller is designed into a self-organizing style. Thirdly, the criterions of growing and pruning network are designed considering the characteristic of WWTP. The adaptive laws of DANC parameters are obtained through the Lyapunov method. Simulation experiments show that the control accuracy and dynamic performance of DO concentration under DANC are improved. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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