Using CMAC for adaptive nonlinear MPC and optimal setpoint identification of an activated sludge process
| Autor: | Mahsa Sadeghassadi, Chris J. B. Macnab |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
021103 operations research Computer science 0211 other engineering and technologies Linear model Process (computing) 02 engineering and technology Nonlinear programming Setpoint Nonlinear system Model predictive control 020901 industrial engineering & automation Control theory Trajectory |
| Zdroj: | SSCI |
| DOI: | 10.1109/ssci.2017.8280852 |
| Popis: | This paper proposes both an adaptive nonlinear model predictive control and a method to identify an optimal setpoint. Local discrete-time linear models, estimated from output measurements, are stored in a Cerebellar Model Arithmetic Computer (CMAC). The CMAC provides a practical way to store, access, and interpolate the models in real-time and for future-time predictions. A finite-horizon nonlinear optimization decides on a desired control signal for training a CMAC controller. In order to search for on an optimal setpoint in the case of a measured disturbance, another set of local linear models is produced that depends on only outputs and disturbances. A Lyapunov-based method ensures stability (uniformly ultimately bounded signals) in the cases of a cart-pendulum system and an activated sludge process for wastewater treatment. Simulation results show successful trajectory tracking and setpoint identification for both systems in simulation. |
| Databáze: | OpenAIRE |
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