Probabilistically valid stochastic extensions of deterministic models for systems with uncertainty

Konstantinos Karydis, Ioannis Poulakakis, Jianxin Sun, Herbert G. Tanner
2015 The international journal of robotics research  
Models capable of capturing and reproducing the variability observed in experimental trials can be valuable for planning and control in the presence of uncertainty. This paper reports on a new data-driven methodology that extends deterministic models to a stochastic regime and offers probabilistic guarantees. From an acceptable deterministic model, a stochastic one is generated, capable of capturing and reproducing uncertain system-environment interactions at given levels of fidelity. The
more » ... ed approach combines methodological elements of probabilistic model validation and randomized algorithms, to simultaneously quantify the fidelity of a model and tune the distribution of random parameters in the augmented stochastic extension, in order to reproduce the variability observed experimentally in a physical process of interest. The approach can apply to an array of physical processes, the models of which may come in different forms, including differential equations; we demonstrate this point by considering examples from the areas of miniature legged robots and aerial vehicles.
doi:10.1177/0278364915576336 fatcat:motijrfnonaejhaa4qvyhtmegi