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Found 7 papers, 2 papers with code
A Robust, Efficient Predictive Safety Filter
In this paper, we propose a novel predictive safety filter that is robust to bounded perturbations and is implemented in an even-triggered fashion to reduce online computation.
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of PIML models; and 4) physics-informed digital twins.
Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles
Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs).
Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach
We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions.
High-order Barrier Functions: Robustness, Safety and Performance-Critical Control
Furthermore, the proposed formulation accounts for "performance-critical" control: it guarantees that a subset of the forward invariant set will admit any existing, bounded control law, while still ensuring forward invariance of the set.
Safe-by-Design Control for Euler-Lagrange Systems
The proposed approach consists of a sampled-data controller and an algorithm for barrier function construction to enforce safety (i. e satisfy position and velocity constraints).
On Compatibility and Region of Attraction for Safe, Stabilizing Control Laws
A novel control method is proposed to ensure compatibility of safe, stabilizing control laws, i. e., simultaneous satisfaction of asymptotic stability and constraint satisfaction for nonlinear affine systems.
