Model Free Adaptive Control Algorithm based on GRU network

The application of traditional adaptive control algorithm usually depends on the precise mathematical model of process, but it is difficult to establish a mathematical model for a dynamic process. The existing Model Free Adaptive (MFA) control algorithm structure is based on Back Propagation(BP) Neural Network, which does not fully take into account the continuous characteristic relationship between the input timing error sequences. According to this actual problem, an improved MFA control algorithm based on Gated Recurrent Unit (GRU) network is proposed in this paper, compared with BP network, GRU network has more advantages in processing timing series. It fully considers the information of error sequence, and more appropriate manipulated variable could be generated automatically to satisfy the demand of an open-loop stable and controllable single-variable, multivariable or large delay process system without the need for complicated manual adjustments, quantitative knowledge of the process or the identifier of the controlled system and learning process. Simulation results demonstrate that the GRU-MFA learning algorithm performs better in terms of stability, response speed and adaptability than the BP-MFA algorithm without human intervention.