Learning control of fermentation process with an improved DHP algorithm

Control of the fed-batch ethanol fermentation processes to produce maximum product ethanol is one of the key issues in the bioreactor system. However, ethanol fermentation processes exhibit complex behavior and nonlinear dynamics with respect to the cell mass, substrate, feed-rate, etc. An improved dual heuristic programming algorithm based on the least squares temporal difference with gradient correction (LSTDC) algorithm (LSTDC-DHP) is proposed to solve the learning control problem of a fed-batch ethanol fermentation process. As a new algorithm of adaptive critic designs, LSTDC-DHP is used to realize online learning control of chemical dynamical plants, where LSTDC is commonly employed to approximate the value functions. Application of the LSTDC-DHP algorithm to ethanol fermentation process can realize efficient online learning control in continuous spaces. Simulation results demonstrate the effectiveness of LSTDC-DHP, and show that LSTDC-DHP can obtain the near-optimal feed rate trajectory faster than other-based algorithms.