Controller Architectures based on Learning Patterns
Main Idea: Identifying and using patterns in systems can enable a combination of model-based and model-free methods for decision-making which are more efficient in data consumption and computation time.
We study and improve the tradeoff between model-free (data-driven) methods and model-based methods of stochastic control and estimation.
- Model-free control methods can generalize better to a variety of complex stochastic systems (e.g., via neural networks), at the cost of intensive data consumption and training time.
- Model-based control methods may require less data and training (due to being characterized by explicit models), at the cost of being reliant on simplifying assumptions.
For example, stochastic model-based controllers typically assume the random process can be modeled using Gaussian distributions, and Gaussian white noise models, such as linear quadratic Gaussian (LQG) control and Kalman filtering, have been shown to work well in practice for many applications.
However, they cannot be used to model impulsive jump perturbations such as wind gusts, large proprioceptive errors, sudden path deviations due to obstacle collisions, etc.
While model-free methods could be used to learn the jump dynamics as a black box, a more efficient process would be to try Poisson and Lévy processes as candidate models, and augment from there.
Motivated by this, we identify and use patterns to develop autonomous control and estimation algorithms that are more efficient in data consumption and computation time.
Representative work(s):
- SooJean Han, Soon-Jo Chung, John C. Doyle, "Predictive Control of Linear Discrete-Time Markovian Jump Systems by Learning Recurrent Patterns." Automatica, 2023.
- SooJean Han, Soon-Jo Chung, "Incremental Nonlinear Stability Analysis for Stochastic Systems Perturbed by Lévy Noise." International Journal of Robust and Nonlinear Control, 2022.