Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
This work addresses control challenges in robotics by improving robustness in model-based methods, but it is incremental as it builds on existing feedback linearization and GP techniques.
The paper tackled trajectory tracking control for mechanical systems using black-box Gaussian process models with feedback linearization, comparing two strategies on a simulated 7-DOF manipulator and finding the second strategy more robust to kernel choice and model inaccuracies, with polynomial kernels performing best.
In this paper, we consider the use of black-box Gaussian process (GP) models for trajectory tracking control based on feedback linearization, in the context of mechanical systems. We considered two strategies. The first computes the control input directly by using the GP model, whereas the second computes the input after estimating the individual components of the dynamics. We tested the two strategies on a simulated manipulator with seven degrees of freedom, also varying the GP kernel choice. Results show that the second implementation is more robust w.r.t. the kernel choice and model inaccuracies. Moreover, as regards the choice of kernel, the obtained performance shows that the use of a structured kernel, such as a polynomial kernel, is advantageous, because of its effectiveness with both strategies.