Enhancing Model Reference Adaptive Control through Error Prediction using Phase Space Reconstruction and GRU Network

In this paper, a novel approach is proposed to enhance model reference adaptive control (MRAC) systems by incorporating error prediction through phase space reconstruction (PSR) and a gated recurrent unit (GRU) neural network. Traditional adaptive control systems often struggle with unpredictable disturbances and uncertainties in dynamic processes. Here, we leverage the power of phase space reconstruction to capture underlying system dynamics and employ a GRU network to predict future error states. By integrating these techniques into adaptive control, our proposed method demonstrates superior adaptability and robustness, enabling more efficient and reliable control in complex and uncertain environments. Experimental findings confirm the efficacy of the suggested strategy, highlighting its potential for complex real-world applications.