Output-Tracking Explicit Nonlinear Model Predictive Control for Microbial Desalination Cells

摘要

Microbial fuel cells (MFC) is a new technique for the environmental protection and new energy. Microbial desalination cells (MDC) is a kind of MFC which has the function of desalination while producing electricity. The design of control strategies is a scarcity part in the field of microbial fuel cells. This paper presents to design an explicit model predictive controller for a kind of microbial fuel cell, which uses the machine learning method for easy to implement. Also, a systematic data-driven control method is presented for the design of explicit model predictive controller for time-varying output tracking in nonlinear model systems. The design consists of (1) sampling the admissible state space by the mathematical model to make the controller better suited to the model; (2) solving for optimal model predictive control actions at each sampled data point and determining feasible region of the nonlinear programming problem; and (3) constructing the control surface of explicit model predictive controller using artificial neural network. In particular, the designed control algorithm is performed on a seven-dimensional mathematical model of microbial desalination cells to test the good control performance.