Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes
This work provides an automated and computationally efficient solution for system identification in robotics and adaptive control, though it appears incremental as it combines existing techniques like NARX, filtered regressors, and sparse Gaussian processes.
The authors tackled the problem of nonlinear system identification by introducing GP-FNARX, a model that integrates data pre-processing with system identification into an automated procedure, resulting in a Bayesian model that reports uncertainty in data-scarce regions with relatively low computational cost.
We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is tackled using sparse Gaussian processes (GP). We integrate data pre-processing with system identification into a fully automated procedure that goes from raw data to an identified model. Both pre-processing parameters and GP hyper-parameters are tuned by maximizing the marginal likelihood of the probabilistic model. We obtain a Bayesian model of the system's dynamics which is able to report its uncertainty in regions where the data is scarce. The automated approach, the modeling of uncertainty and its relatively low computational cost make of GP-FNARX a good candidate for applications in robotics and adaptive control.