A Reduced-Dimensional System Identification Framework With Latent State-Space Models

Large-dimensional systems are ubiquitous in industrial manufacturing systems, smart grids, and biological systems. This article develops a reduced-dimensional latent state-space identification (LaSSID) framework for systems where the output dynamics is in a low-dimensional latent space. The proposed LaSSID algorithm effectively handles collinear dynamics among output variables and improves the condition number. The method is compared to traditional methods in terms of enhanced predictability and interpretability. In addition, a modified system identification experiment is developed using the Tennessee Eastman process simulation, incorporating reduced-dimensional excitations and collinear perturbations to simulate real-world scenarios. The results of this simulated experiment and the Dow Challenge industrial dataset demonstrate the superior performance of the proposed algorithm in terms of system identification accuracy and robustness to noise.