Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview
Contraction theory is an analytical tool to study differential dynamics of a non-autonomous
(ie, time-varying) nonlinear system under a contraction metric defined with a uniformly …
(ie, time-varying) nonlinear system under a contraction metric defined with a uniformly …
Learning stable Koopman embeddings
In this paper, we present a new data-driven method for learning stable models of nonlinear
systems. Our model lifts the original state space to a higher-dimensional linear manifold …
systems. Our model lifts the original state space to a higher-dimensional linear manifold …
Stable linear subspace identification: A machine learning approach
Machine Learning (ML) and linear System Identification (SI) have been historically
developed independently. In this paper, we leverage well-established ML tools—especially …
developed independently. In this paper, we leverage well-established ML tools—especially …
Stability guarantees for continuous rl control
Lack of stability guarantees strongly limits the use of reinforcement learning (RL) in safety
critical robotic applications. Here we propose a control system architecture for continuous …
critical robotic applications. Here we propose a control system architecture for continuous …
Robustness analysis and training of recurrent neural networks using dissipativity theory
Neural networks are widely applied in control applications, yet providing safety guarantees
for neural networks is challenging due to their highly nonlinear nature. We provide a …
for neural networks is challenging due to their highly nonlinear nature. We provide a …
Neural-rendezvous: Learning-based robust guidance and control to encounter interstellar objects
Interstellar objects (ISOs), astronomical objects not gravitationally bound to the Sun, are
likely representatives of primitive materials invaluable in understanding exoplanetary star …
likely representatives of primitive materials invaluable in understanding exoplanetary star …
Uncertainty learning for lti systems with stability guarantees
We present a framework for learning of modeling uncertainties in Linear Time Invariant (LTI)
systems to improve the predictive capacity of system models in the input-output sense. First …
systems to improve the predictive capacity of system models in the input-output sense. First …
Learning first principles systems knowledge from data: Stability and safety with applications to learning from demonstration
The chapter presents two methods of constrained learning of dynamical system models from
data with applications to learning from demonstration (LfD) or imitation learning in the …
data with applications to learning from demonstration (LfD) or imitation learning in the …
Matrix Measure Flows: A Novel Approach to Stable Plasticity in Neural Networks
This letter introduces the notion of a matrix measure flow as a tool for analyzing the stability
of neural networks with time-varying weights. Given a matrix flow--for example, one induced …
of neural networks with time-varying weights. Given a matrix flow--for example, one induced …
On the improvement of model-predictive controllers
L Féret, A Gepperth, S Lambeck - arxiv preprint arxiv:2308.15157, 2023 - arxiv.org
This article investigates synthetic model-predictive control (MPC) problems to demonstrate
that an increased precision of the internal prediction model (PM) automatially entails an …
that an increased precision of the internal prediction model (PM) automatially entails an …