Model predictive control of microbial fuel cell based on Kalman state estimation

Abstract

Abstract Aiming at the constraints and undetectable interference in the microbial fuel cell system, a microbial fuel cell model predictive control method based on state estimation is proposed. According to the principles and actual requirements of the microbial fuel cell system, a state space model with input constraints is established. By introducing the model predictive controller, the performance of constrained optimization control is improved. Combined with the Kalman filter estimator, the impact of unmeasured interference on the predictive controller is compensated, and the control accuracy and robustness of the system are improved. The simulation experiment finally indicate that model predictive control based on kalman state estimation makes the output voltage of system reach the desired value, the input flow meets the actual demand and the cost is optimal. In addition, it has a good ability to deal with interference.

Key findings

• ▸The proposed method improves the performance of constrained optimization control in microbial fuel cells.
• ▸The Kalman filter estimator compensates for unmeasured interference, enhancing control accuracy and robustness.
• ▸The method successfully achieves optimal output voltage, input flow, and cost in simulation experiments.

Keywords

Identifiers

DOI

10.1088/1742-6596/1848/1/012063

Journal

Journal of Physics: Conference Series

Year

2021