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Found 12 papers, 5 papers with code
Verification-Aided Learning of Neural Network Barrier Functions with Termination Guarantees
With a convex formulation of the barrier function synthesis, we propose to first learn an empirically well-behaved NN basis function and then apply a fine-tuning algorithm that exploits the convexity and counterexamples from the verification failure to find a valid barrier function with finite-step termination guarantees: if there exist valid barrier functions, the fine-tuning algorithm is guaranteed to find one in a finite number of iterations.
Learning Performance-Oriented Control Barrier Functions Under Complex Safety Constraints and Limited Actuation
Control Barrier Functions (CBFs) provide an elegant framework for designing safety filters for nonlinear control systems by constraining their trajectories to an invariant subset of a prespecified safe set.
PcLast: Discovering Plannable Continuous Latent States
Goal-conditioned planning benefits from learned low-dimensional representations of rich, high-dimensional observations.
Safety Filter Design for Neural Network Systems via Convex Optimization
With the increase in data availability, it has been widely demonstrated that neural networks (NN) can capture complex system dynamics precisely in a data-driven manner.
Robust Model Predictive Control of Time-Delay Systems through System Level Synthesis
In the proposed method, a time-varying feedback control policy is optimized such that the robust satisfaction of all constraints for the closed-loop system is guaranteed.
One-Shot Reachability Analysis of Neural Network Dynamical Systems
The arising application of neural networks (NN) in robotic systems has driven the development of safety verification methods for neural network dynamical systems (NNDS).
Differentiable Safe Controller Design through Control Barrier Functions
Learning-based controllers, such as neural network (NN) controllers, can show high empirical performance but lack formal safety guarantees.
Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis
However, it is challenging to design LTV state feedback controllers in the face of model uncertainty whose effects are difficult to bound.
System Level Synthesis-based Robust Model Predictive Control through Convex Inner Approximation
We propose a robust model predictive control (MPC) method for discrete-time linear time-invariant systems with norm-bounded additive disturbances and model uncertainty.
Learning Region of Attraction for Nonlinear Systems
Estimating the region of attraction (ROA) of general nonlinear autonomous systems remains a challenging problem and requires a case-by-case analysis.
DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting
Analyzing the worst-case performance of deep neural networks against input perturbations amounts to solving a large-scale non-convex optimization problem, for which several past works have proposed convex relaxations as a promising alternative.
Learning Lyapunov Functions for Hybrid Systems
By designing the learner and the verifier according to the analytic center cutting-plane method from convex optimization, we show that when the set of Lyapunov functions is full-dimensional in the parameter space, our method finds a Lyapunov function in a finite number of steps.
Optimization and Control
