Development of modeling and control strategies for an approximated Gaussian process
This work addresses computational bottlenecks in Gaussian process modeling for control applications, but it appears incremental as it builds on existing approximation methods.
The authors tackled the computational inefficiency of Gaussian process models by proposing a linear approximation using basis functions, which improved computational efficiency and enabled the implementation of a control strategy as demonstrated in simulation studies.
The Gaussian process (GP) model, which has been extensively applied as priors of functions, has demonstrated excellent performance. The specification of a large number of parameters affects the computational efficiency and the feasibility of implementation of a control strategy. We propose a linear model to approximate GPs; this model expands the GP model by a series of basis functions. Several examples and simulation studies are presented to demonstrate the advantages of the proposed method. A control strategy is provided with the proposed linear model.